andreas gohs giselmar hemmert michael holtermann jérôme
TRANSCRIPT
Andreas Gohs
Giselmar Hemmert Michael Holtermann
Jérôme Massiani Jens Weinmann
EMOB Technical Documentation
No. 01/NL/2015 ISSN 2281-6577
Working notes Department of Economics
Ca’ Foscari University of Venice No. 0 1 /NL/201 5
ISSN 2281-6577
EMOB,&technical&documentation
Andreas Gohs Giselmar Hemmert
Michael Holtermann Jérôme MASSIANI
Jens Weinmann
All authors were working at ESMT when participating tot the project. J . Massiani was addit ionally working at Università Cà
Foscari di Venezia
Abstract This report is the extended version of the technical documentation of the Market Model
Elektro Mobilitaet research project and the EMOB model that was developed in this
framework. This report was made available in dec. 2014 to allow in-depth investigation
and understanding of the project and the model.
The research project has been completed thanks to contract 16EM0039. The content of
this publication is the only responsibility of the authors.
Jé rôme Mass ian i Department of Economics
Ca ’ Foscar i Un ivers i ty o f Ven ice Cannaregio 873, Fondamenta S.Giobbe
30121 Venezia - Italy Phone: (++39) 041 2349234
Fax: (++39) 041 2349176 e-mail: [email protected]
This Working notes are published under the auspices of the Department of Economics of the Ca’ Foscari University of Venice. Opinions expressed herein are those of the authors and not those of the Department. The Working Paper series is designed to divulge preliminary or incomplete work, circulated to favour discussion and comments.
Content
1.! Purpose of this document ....................................................................................... 13!
2.! Existing Approaches for Simulation of Alternative Fuel Vehicles Market Penetration .............. 14!
2.1.! Total Cost of Ownership (TCO) ................................................................................... 14!
2.2.! Diffusion Theory .................................................................................................... 15!
2.3.! Stated Preferences surveys ....................................................................................... 16!
2.4.! Existing models ..................................................................................................... 17!
2.5.! Electric car evaluation ............................................................................................ 18!
2.6.! Review result ....................................................................................................... 19!
3.! Car Market in MMEM .............................................................................................. 21!
3.1.! Conceptual framework for the representation of the car market .......................................... 21!
3.2.! Aggregated car demand ........................................................................................... 21!
3.2.1.! Segment choice by size ............................................................................................ 21!
3.2.2.! Choice probability generation .................................................................................... 22!
3.2.3.! Nesting structure – choice of nesting parameters ............................................................. 23!
3.2.4.! Technology and segment choice modeling ..................................................................... 25!
3.2.5.! Willingness to pay for car attributes in the household purchase model ................................... 26!
Autonomy ........................................................................................................................ 28!
Range 28!
Refueling facility ............................................................................................................... 29!
Willingness to pay for Ultra-Fast charging ................................................................................ 30!
Car performance ............................................................................................................... 30!
Environmental features (emissions) ........................................................................................ 31!
Variable costs ................................................................................................................... 31!
Fuel costs 31!
Road tax costs ................................................................................................................... 31!
Parking charges (and their possible exemptions) ........................................................................ 31!
Costs of private parking hire ................................................................................................. 32!
3.2.6.! Household segmentation .......................................................................................... 33!
Purchase costs and other fixed costs ...................................................................................... 34!
Expected wall box costs ...................................................................................................... 34!
Battery replacement .......................................................................................................... 34!
3.2.7.! Price elasticity ...................................................................................................... 34!
4.! Diffusion approach in MMEM .................................................................................... 35!
4.1.! Theoretical background of diffusion theories .................................................................. 35!
4.2.! Modeling diffusion in MMEM ....................................................................................... 36!
4.3.! Values for p and q .................................................................................................. 38!
Expert view ...................................................................................................................... 38!
Cao, 2004 38!
Lamberson ....................................................................................................................... 39!
Steffens, 2003 .................................................................................................................. 40!
Becker, 2009 .................................................................................................................... 40!
Gross, 2008 ...................................................................................................................... 40!
4.4.! Estimation based on market data ................................................................................ 40!
Available data ................................................................................................................... 40!
4.5.! Estimation with model-endogenous market potential ........................................................ 42!
4.6.! Conclusion on Bass parameters .................................................................................. 44!
Estimation with model-exogenous market potential .................................................................... 44!
Discussion of the results ...................................................................................................... 46!
5.! Car industry and CO2 optimization in MMEM ................................................................ 48!
EU-Regulation 443/2009 ...................................................................................................... 48!
Technical development of cars .............................................................................................. 49!
Manufacturer costs of emission reduction ................................................................................ 49!
Customer benefits of emission reduction ................................................................................. 49!
Manufacturers’ maxim of acting in MMEM ................................................................................. 50!
Expected impact of optimization measures on the market ........................................................... 51!
Figure 29: Adapted TNO-Curve Gasoline small ........................................................................... 53!
Figure 35: Adapted TNO-Curve Hybrid small ............................................................................. 55!
6.! Energy sector ....................................................................................................... 56!
6.1.! Introduction ......................................................................................................... 56!
6.2.! Electricity prices ................................................................................................... 57!
6.3.! Oil prices ............................................................................................................ 58!
6.4.! Electricity supply ................................................................................................... 58!
6.4.1.! Reference scenario ................................................................................................. 59!
6.4.2.! SRU 509TWh scenario .............................................................................................. 59!
6.4.3.! MMEM 100% renewables scenario ................................................................................ 60!
6.4.4.! Simulation of fluctuating primary energies .................................................................... 61!
6.5.! Electricity demand ................................................................................................. 67!
6.6.! Matching supply and demand: Merit-order dispatch .......................................................... 67!
6.7.! Charging patterns of electric vehicles .......................................................................... 70!
6.8.! Coordinated and uncoordinated charging ...................................................................... 73!
6.9.! Non-tailpipe emissions ............................................................................................. 75!
6.9.1.! Literature overview ................................................................................................ 75!
6.9.2.! Methods for computation of CO2 emissions ..................................................................... 77!
6.9.3.! Computation of CO2 emissions .................................................................................... 82!
6.9.4.! Power consumption and grid investments ...................................................................... 88!
6.9.5.! Charging infrastructure ............................................................................................ 93!
7.! Cost Benefit Assessment ......................................................................................... 96!
7.1.! Introduction and General Approach ............................................................................. 96!
7.2.! Discounting rate and time horizon ............................................................................... 96!
7.2.1.! Setting a discounting rate ......................................................................................... 96!
7.3.! Policy Scenario ...................................................................................................... 97!
7.4.! Impacts considered ................................................................................................ 98!
7.5.! Consumers net benefits in the policy scenario ................................................................ 99!
7.5.1.! Changes in car purchase expenditure of the policy ........................................................... 99!
7.5.2.! Fuel costs expenditure ........................................................................................... 102!
7.6.! Infrastructure costs ............................................................................................... 104!
7.6.1.! Home charging infrastructure ................................................................................... 104!
7.6.2.! Grid investments .................................................................................................. 106!
Figure 82 Grid investment spending ..................................................................................... 107!
7.7.! Government finances ............................................................................................. 107!
7.7.1.! Direct policy costs ................................................................................................. 108!
7.7.2.! Energy tax revenues .............................................................................................. 108!
7.7.3.! VAT tax revenues .................................................................................................. 110!
7.7.4.! Road tax income ................................................................................................... 110!
7.7.5.! Opportunity costs of public funding ............................................................................ 111!
7.8.! Industry ............................................................................................................. 112!
7.9.! Environmental impact ............................................................................................ 114!
7.9.1.! CO2 emissions ...................................................................................................... 114!
7.9.2.! Abatement costs ................................................................................................... 116!
7.9.3.! Other tailpipe-pollutants ......................................................................................... 117!
7.10.! Overall assessment and conclusions ............................................................................ 118!
8.! Economic impact assessment ................................................................................. 119!
8.1.! Introduction – Basis set up and requirements ................................................................. 119!
8.2.! Economic Modeling Approach – Direct impacts ............................................................... 120!
9.! Car attributes .................................................................................................... 124!
9.1.! Definition of technologies ....................................................................................... 124!
Conventional vehicles (ICE) ................................................................................................ 124!
Battery electric vehicles (BEV) ............................................................................................ 124!
Hybrid electric vehicles (HEV) ............................................................................................. 124!
Plug-in hybrid electric vehicles (PHEV) .................................................................................. 124!
Range extenders (REV) ...................................................................................................... 124!
Biofuel vehicles ............................................................................................................... 124!
LPG vehicles ................................................................................................................... 124!
Fuel cell electric vehicles (FCEV) ......................................................................................... 124!
Plug-in-Hybrids and Range Extenders .................................................................................... 124!
9.2.! Segmentation ...................................................................................................... 125!
9.3.! Attributes in buying decisions ................................................................................... 126!
9.3.1.! Gasoline cars ....................................................................................................... 127!
9.3.2.! Diesel cars .......................................................................................................... 128!
9.3.3.! Biofuel E85 vehicles ............................................................................................... 129!
9.3.4.! LPG cars ............................................................................................................ 129!
9.3.5.! Hydrogen cars (FCEV) ............................................................................................. 130!
9.3.6.! BEV .................................................................................................................. 130!
9.4.! Purchase prices .................................................................................................... 132!
9.4.1.! Conventional cars ................................................................................................. 132!
9.4.2.! Biofuel E85 cars .................................................................................................... 132!
9.4.3.! LPG cars ............................................................................................................ 132!
9.4.4.! Non-internal combustion engine cars .......................................................................... 132!
Non-battery extra prices ................................................................................................... 132!
We rely on the non-battery extra costs based on information reported in (Blesl M., Bruchof D. et al. 2009)
...................................................................................................................... 132!
9.4.5.! Battery prices ...................................................................................................... 134!
9.5.! Fuel costs and fuel consumptions ............................................................................... 134!
9.5.1.! Conventional cars ................................................................................................. 134!
9.5.2.! BEV .................................................................................................................. 135!
9.5.3.! Hybrids .............................................................................................................. 136!
9.5.4.! PHEV and RE ....................................................................................................... 136!
9.5.5.! Hydrogen vehicles ................................................................................................. 137!
9.5.6.! Biofuel E85 vehicles ............................................................................................... 137!
9.5.7.! LPG vehicles ........................................................................................................ 138!
9.6.! Horsepower ......................................................................................................... 138!
9.6.1.! Conventional vehicles and BEV .................................................................................. 138!
9.6.2.! Hybrids .............................................................................................................. 138!
9.6.3.! PHEV and RE ....................................................................................................... 138!
9.6.4.! Hydrogen cars ...................................................................................................... 138!
9.6.5.! Biofuel E85 cars .................................................................................................... 139!
9.6.6.! LPG cars ............................................................................................................ 139!
9.7.! CO2 – tailpipe emissions .......................................................................................... 139!
9.7.1.! Conventional vehicles ............................................................................................ 139!
9.7.2.! Hybrids .............................................................................................................. 139!
9.7.3.! Range Extenders ................................................................................................... 139!
9.8.! Range ................................................................................................................ 139!
9.8.1.! Conventional vehicles ............................................................................................ 139!
9.8.2.! BEV .................................................................................................................. 139!
9.8.3.! Hybrids .............................................................................................................. 140!
9.8.4.! PHEV and RE ....................................................................................................... 140!
Range based on fuel use: ................................................................................................... 140!
9.8.5.! Hydrogen vehicles ................................................................................................. 140!
9.8.6.! Biofuel E85 vehicles ............................................................................................... 140!
9.8.7.! LPG vehicles ........................................................................................................ 141!
10.! Battery technologies ............................................................................................ 142!
10.1.! Energy content per battery pack and weight ................................................................. 142!
10.2.! State-of-charge window .......................................................................................... 144!
10.3.! Battery kWh unit costs ........................................................................................... 144!
10.4.! Battery weights and capacities ................................................................................. 145!
10.5.! Battery pack price ................................................................................................ 147!
11.! Fuel costs ......................................................................................................... 149!
11.1.! Electricity prices in MMEM ....................................................................................... 149!
11.2.! Oil price ............................................................................................................. 149!
11.3.! Gasoline and diesel price in MMEM ............................................................................. 149!
11.4.! Biofuel E85 production costs and prices ....................................................................... 151!
11.5.! Liquefied Petroleum Gas (LPG) price .......................................................................... 153!
11.6.! Hydrogen price .................................................................................................... 154!
Concerning the development of the hydrogen price ................................................................. 155!
12.! Appendix .......................................................................................................... 156!
Further information on car attributes ................................................................................... 156!
12.1.! Best seller Gasoline and Diesel cars according to KBA/ADAC .............................................. 156!
12.2.! Manufacturers and consumers statements about fuel consumptions of gasoline and diesel vehicles
....................................................................................................................... 157!
12.2.1.! Statments from other sources about car prices .............................................................. 158!
12.2.2.! Car price development ........................................................................................... 160!
12.2.3.! Calculation of excess car prices from data provided by Blesl .............................................. 161!
12.2.4.! Extra purchase prices of Bioethanol vehicles ................................................................. 163!
12.3.! Energy content and power of batteries ........................................................................ 164!
12.3.1.! Nameplate and available capacity .............................................................................. 164!
12.3.2.! Battery unit costs ................................................................................................. 166!
12.3.3.! Battery sizes ....................................................................................................... 167!
12.3.4.! Characteristics of selected BEV ................................................................................. 168!
12.3.5.! The weights of battery electric vehicles ...................................................................... 169!
12.3.6.! Energy consumption of BEV ...................................................................................... 171!
12.4.! Battery capacities of Plug-in hybrid electric vehicles and Range extenders ............................. 171!
12.5.! Energy consumption of Plug-in hybrid electric vehicles and Range extenders .......................... 172!
12.6.! Fuel consumption of hydrogen vehicles ....................................................................... 174!
12.7.! Fuel consumption of vehicles running on Biofuel (E85) ..................................................... 174!
12.8.! Consumption of vehicles running on LPG ...................................................................... 175!
HP Hybrids ....................................................................................................................... 176!
HP bioethanol (E85) vehicles ................................................................................................. 176!
Hydrogen technology .......................................................................................................... 177!
Hydrogen refueling stations and prices ..................................................................................... 180!
Alternative fuel refueling stations .......................................................................................... 180!
Further Information on fuel prices .......................................................................................... 182!
For a comparison: Fuel price scenarios in other studies ................................................................. 182!
Motor vehicle tax, petroleum tax, electricity tax, Value added tax (VAT) ........................................... 182!
Composition of fuel prices .................................................................................................... 185!
Bioethanol price trends ....................................................................................................... 186!
References ..................................................................................................................... 189!
Projektteam ................................................................................................................... 194!
About ESMT .................................................................................................................... 195!
Tables and Figures
Table 1: Main existing models for the forecast and evaluation of electric car diffusion 18!Table 2: Wheelbase by segments 22!Figure 3: MMEM nested logit structure 24!Table 4: Set of SP surveys used in MMEM 26!Table 5: WTP in MMEM 27!Figure 6: WTP for range 28!Figure 7: Marginal willingness-to-pay for range based on various studies 29!Table 8: market value of extra Horsepower 30!Figure 9: Parking expenditure and cost by area 32!Figure 10: Breakdown of car ownership by availability of parking 33!Table 11: Overview assumed parking hire costs 33!Table 12: Assumed battery life time 34!Figure 13: MMEM Model results for market potential and realized market shares of PHEV 37!Table 14: Bass coefficient values estimated by (Cao 2004) 39!Table 15: Coefficient values for a Bass diffusion model and a Gompertz diffusion model estimated by
Lamberson 2008 40!Figure 16: Cumulative vehicle new registrations for different technologies (Dec. 2008 – Jan. 2011) 41!Table 17: Bass parameter estimation issues 43!Table 18: Estimated Bass p-parameter values for different model-exogenously determined market potentials
44!Table 19: Estimated Bass q-parameter values for different model-exogenously determined market potentials
44!Table 20: Estimated p- and q parameter for different market potentials – annual time series - Hybrid 45!Figure 21: cumulative Toyota sales volumes 1997 - 2010 45!Figure 22: Estimated regression coefficients and Bass’ p, q and m coefficients for Toyota Hybrids 46!Table 23: Bass parameter MMEM conclusion 46!Figure 24: Adapted TNO-Curve Gasoline large 53!Figure 25: Adapted TNO-Curve Gasoline medium 53!Figure 26: Adapted TNO-Curve Gasoline small 53!Figure 27: Adapted TNO-Curve Diesel large 54!Figure 28: Adapted TNO-Curve Diesel medium 54!Figure 29: Adapted TNO-Curve Diesel small 54!Figure 30: Adapted TNO-Curve Hybrid large 55!Figure 31: Adapted TNO-Curve Hybrid medium 55!Figure 32: Adapted TNO-Curve Hybrid small 55!Figure 33: Oil price scenarios of the EIA 58!Figure 34: Capacity forecast in the EWI-Prognos reference scenario 59!Figure 35: Capacity forecast in the SRU 509 TWh scenario 60!Figure 36: Capacity forecast in the MMEM 100% Renewables scenario 61!Table 37: Annual availability of conventional and renewable generation technologies 61!Figure 38: Annual fluctuations of wind intake 62!Figure 39: Observed values and estimated distribution for period January to March 63!Figure 40: Observed values and estimated distribution for period April to June 64!Figure 41: Observed values and estimated distribution for period July to September 64!
Figure 42: Observed values and estimated distribution for period October to December 65!Figure 43: Average daily solar intake according to grid operator data 66!Figure 44: Standard deviations of average daily solar intake according to grid operator data 66!Figure 45: Differences in the fluctuations of weekly demand, according to quarters 67!Figure 46: Long-term marginal cost development of selected technologies 68!Figure 47: Exemplary merit-order dispatch curve for Germany in 2020 69!Figure 48: Generation deployment at 6pm according to Prognos-EWI-GWS capacity forecasts 70!Figure 49: Weekly mobility patterns of private car owners 71!Figure 50: Histograms for total daily trip length, split according to weekday, Saturday and Sunday 72!Figure 51: Aggregated charging requirements of pure battery electric vehicle, sample week 2020 73!Figure 52: Difference in EV loads due to uncoordinated and coordinated charging 74!Table 53: Electric vehicle non-tailpipe emissions across various studies 77!Table 54: Methods used for emission calculations of EV 80!Figure 55: Simplified representation of pivotal technologies and additional demand 82!Figure 56: Pivotal technologies share in the MMEM reference scenario with EWI-Prognos portfolio 83!Figure 57: Specific emissions of electric vehicles in the MMEM reference scenario, calculated for 6 charging
alternatives 84!Figure 58: Pivotal technologies share in the SRU 509 TWh scenario 85!Figure 59: Specific emissions of electric vehicles according to the SRU 509 TWh scenario, calculated for 6
charging alternatives 86!Figure 60: Pivotal technologies share in the MMEM 100 % renewables scenario 87!Figure 61: Pivotal technologies share in the MMEM 100 % renewables scenario 87!Figure 62: Specific emissions of electric vehicles according to the MMEM 100 % renewables scenario, calculated
for 6 charging alternatives 88!Figure 63: Share of electric vehicle demand in overall electricity demand in Germany 89!Figure 64: Functional relationship between the density of electric vehicles and grid renewal requirements 90!Table 65: 91!Figure 66: Grid investments due to electric vehicles according to geographic regional characteristics 92!Figure 67: Grid investments due to electric vehicles according to geographic regional characteristics 93!Table 68: Data inputs for the quick charging NPV calculation 94!Figure 69: Number of profitably operating quick charging units in Germany 95!Figure 71 - Electric vehicle stock policy and reference scenario 98!Table 72 Overview impacts considered 99!Figure 73 - Comparison of discounted car purchase expenditure 100!Figure 74 – Discounted consumer welfare change from car purchase expenditure 101!Table 75 Weighted average annual driving distances 103!Figure 76 Fuel consumption expenditure 104!Table 77 Assumptions of equipment costs for users without private parking 105!Figure 78 Aggregated spending on off-street charging devices 106!Figure 79 Grid investment spending 107!Figure 80 Direct policy costs 108!Figure 81 Change in discounted fuel tax revenues 109!Figure 82 Discounted change in energy tax receipts 110!Table 83 Discounted change in Value Added Tax revenues 110!Figure 84 Change in discounted road tax payments 111!Table 85 Assumed profitability relevant industries (2007) 113!Table 85 Net benefits from changes in producer rent 114!Figure 87 Average CO2 emission factors (fleet average and newly registered vehicles), g/km 115!Figure 88 Relative change fleet emission factors 116!Figure 89 CO2 Emissions from EV grid charging, tonnes 117!
Table 90 Damage cost estimates other pollutants 117!Table 91 Other pollutants emission factors 118!Figure 78: Overview MMEM economic impact assessment 120!Figure 92: Breakdown production costs into cost components (2010) 121!Figure 80: Labour productivity (GVA/FTE) in € Tsd. (2007) car manufacturing components 122!Table 81: Productivity assumptions by propulsion technology (based on 2007 data) 122!Table 82: Main structural business indicators by sector (2007) 123!Table 83: Electric drive share of Plug-in Hybrids and Range Extenders 125!Table 84: New registrations of passenger vehicles in each KBA-segment between 2008 and 2010, Source:
Kraftfahrtbundesamt (KBA) 126
Table 85: 126!Table 86: Attribute values of best seller gasoline cars, Source: ADAC search engine requested in Dec. 2010 127
Table 87: 128!Table 88: Attribute values of best seller diesel cars, Source: ADAC search engine requested in Dec. 2010 128
Table 89: 129!Table 90: Comparison of car attributes for gasoline and biofuels. (ADAC Dec. 2010, Gasoline – biofuels) 129!Table 91: Differences in main car attributes for LPG fuel cars and similar gasoline cars (LPG – gasoline) based
on ADAC Autodatenbank (dic. 2010) 129!Table 92: Assumed attribute values of LPG cars 130!Table 93: Assumed attribute values of hydrogen cars 130!Table 94: BEV characteristics 131!Table 95: Non-battery extra costs calculated from (Blesl M., Bruchof D. et al. 2009) 133!Table 96: Energy consumption in MJ/km, bold numbers are based on (Blesl M., Bruchof D. et al. 2009), p. 33,
remaining numbers are interpolated 134!Table 97: Assumed energy consumptions of hydrogen cars
Erro
r! Bookmark not defined.!Table 98: 137!Table 99: Assumed energy consumptions of hydrogen cars 137!Table 100: NPE-scenario for the development of the energy content per battery unit weight, Source: (Nationale
Plattform Elektromobilität 2010), p. 8 143
Table 101: MMEM reference scenario of the energy content per battery unit weight for different points in time,
based on (Nationale Plattform Elektromobilität 2010), p. 8 143!Table 102: MMEM alt. scenario of the energy content per battery unit weight for different points in time, based
on (Nationale Plattform Elektromobilität 2010), p. 8 144!Table 103: Battery kWh unit price development, Source: NPE until 2020 and own assumptions beyond 2020 145
Table 104: Assumed PHEV and RE battery capacities (kWh) 147!Table 105: Electricity price reference scenario, Source: (Prognos-EWI-GWS 2010) 149 Table 106: Annual average fuel prices, Source: (ADAC 2011) 150 Table 107: ARAL gasoline and diesel service station prices and levies, Source: (ARAL 2011) 150
Table 108: Most recent fuel prices reported by (ADAC) 150!Table 109: Calculated E85 consumer prices for the ADAC gasoline 2010 price and different scenarios for ethanol
production costs 152!Table 110: Calculated E85 consumer prices for the ARAL preliminary 2011 gasoline price and different scenarios
for ethanol production costs 152!Table 111: Index of industrial producer prices of industrial products (domestic sales), liquefied petroleum gas (LPG),
used as petrol or combustible, Source: DESTATIS 153
Table 112: Hydrogen cost degression scenario according to (The Connecticut Center for Advanced Technology
Inc. 2011) 155!
13
Abbreviations
xEV All Vehicles with electric components in the drivetrain and possibility to be charged via the electric
power grid
PHEV Plug-in Hybrid Electric Vehicle
BEV Battery Electric Vehicle
REV Range Extender Vehicle
FCEV Fuel Cell Electric Vehicle
TCO Total Cost of Ownership
MMEM Market Model Electric Mobility / Marktmodell Elektromobilität
NPE Nationale Plattform Elektromobilität
14
1. Purpose of this document
This document is a supplement to the result report of the Market Model Electric Mobility project. It describes for
scientist interested in the technicalities of the MMEM how the tool is set up and how different approaches are used.
15
2. Existing Approaches for Simulation of Alternative Fuel Vehicles Market Penetration
The literature regarding the diffusion of electric vehicles consists of several types of material: diffusion forecast
(which typically provide the foreseen development of electric vehicles in a given context), models (that allow for
large scale simulation of various policy scenarios), and evaluations (which provide results about the costs and benefits
of policies).
In this section we provide an overview of the different available methods in use for alternative fuel vehicles diffusion
forecast. Such methods basically pertain to three different paradigms: Total Cost of Ownership, diffusion theory,
Stated Preferences survey.
2.1. Total Cost of Ownership (TCO)
Total Costs of Ownership approach is based on the comparison of the capital and operating costs of different
technologies. In most cases it assigns the demand to the minimal costs technology. While this approach is fairly
simplistic in its assumptions, it has gained popularity especially in studies that come from the industry or from
consulting organizations while it has more limited space in the scientific literature (Mock, Hülsebusch et al. 2009).
Reasons for success rely, in our view to:
! the reliance on data that are fairly available. The method relies indeed on fuels costs, taxations, capital
depreciation (based on vehicle value and some amortizing assumption), parking costs, etc., all items
that, to a certain extent, are fairly available to any professional organization engaged into the study of
automotive market dynamics.
! the strong adherence of the model to economic driven paradigm. The approach fits with a context in
which the “economic dimension” of public choice is found to be prominent.
! the possibility for the T.C.O. calculation to perform very detailed calculation, using a large variety of
available indicators. Furthermore, the method is fairly flexible in that any additional piece of
information can be integrated without the necessity to reshape the whole approach. As a result the
large number of variables provides the method a good face value, and sometimes tends to intimidate
possible criticisms.
However the method suffers from serious drawbacks, especially considering two points:
! Costs monism: the method assumes that car purchase behavior is merely dictated by cost
considerations. This assumption turns out to omit others, non-monetary, attributes which can be found
important in various car purchase settings, and especially in the context of new technologies with very
distinctive non-monetary attributes (think of the range attribute of electric cars), which can be a very
important choice criteria.
! Monolithic behavior: TCO typically assumes that two different decision-makers would make the same
decision in the same choice situation. This heavily contrasts with direct experience, with the observed
diversified structure of car market, and with the state of the art of economic analysis that tends to give
full recognition to the interpersonal variations in behavior. However, some applications of the TCO
model try to overcome this limitation by considering a detailed socio-economic decomposition or
introducing some stochastic component. It is only by expending such features that TCO models can
avoid the mentioned pitfall.
16
2.2. Diffusion Theory
A second series of method relies on the diffusion theories. Such approaches usually rely on mathematical formulations
to represent the progressive diffusion of a technology from a given observed level to a hypothesized potential. From
the different available formulation of this method, the formulation proposed by Bass (Bass 1969; Mahajan, Muller et
al. 1990; Bass 2004) has gained the strongest weight and is nowadays highly influential in the management literature.
Bass approach postulates that new technologies have a given potential which will be reached only progressively,
starting from the introduction phase. The diffusion pattern will be determined by two mechanisms: adoption, by
which people purchase new technology, independent from its intrinsic features and imitation, that is purchase
decisions that are influenced by the diffusion of the technology.
Such mechanisms can be expressed as in equation
n! = !"!!" = p M!N! + q !
!N! M!N!
with
n!: product purchases in period t
N!: cumulative product purchases until period t
M: (cumulative) market potential in product life cycle
p: coefficient of innovation
q: coefficient of imitation.
Literally, (M-Nt) represents the reservoir of clients, that is the difference between the potential and the cumulated
achieved sales. This reservoir translates into sales by the effect of adoption (a fraction of the reservoir adopts the
product at each time period) and imitation (purchases increase when more people are in contact with purchasers).
The Bass diffusion approach has gained a lot of popularity and it is fair to say that it is probably the most widely used
method, at least in academic world (although with a high share of unpublished work: Struben 2004,Richardson,
McAlinden et al. 1999, Cao 2004, Lamberson 2008). Reasons for such a success probably pertain to the weight of
diffusion theory in management and marketing sciences. The method is also attractive because it intrinsically
replicates any available information on the current state of the diffusion. When data on the current sales are
available it is always possible to use them in the calculation of the diffusion pattern, and the model will intrinsically
reproduce these data. This avoids the annoying situation where model outcomes deliver a prevision for the current
time period that is inconsistent with observations. There is however room for discussion on whether this increases the
realism of future forecasts or whether it is rather just an artifact due to the definition of the model, with no
implication on its forecast capability.
Another reason for the success of the method is that it provides very smooth diffusion patterns, with no
discontinuities, which provides usually a good prima facie value to the results.
There are however some limitations to the method. First the model does not in itself provide an estimate of the
potential. Actually this point deserves more discussion as various applications of the method use also a calibrate M,
together with p and q, based on observed time series. But this procedure is not to be considered as inherent to the
Bass approach but rather as a statistical expedient to identify the potential.
There are also some issues about the nature of the market potential M that is defined as the cumulative lifetime
potential for the product (first time purchases). This raises questions in situations where products have a long
lifetime: when considering the car industry the notion of total cumulated sales for a technology is a challenging if not
an unrealistic one: on how many years should these sales be computed ? Correspondingly, it is inherent to the Bass
model that the product sales are modeled on the whole life cycle of the product. There will be a startup phase, a
17
mature product development, a declining phase of the sales. This implies that the Bass diffusion model will produce a
phase where sales decrease. This may be difficult to accept for products with a very long time horizon.
Also, from a conceptual point of view, the model seems unrealistically unresponsive to changes in competing
alternatives. Competing alternatives can only impact through M (market potential).
It is also sometimes argued that Bass is unable to forecast product failures. This statement needs to be qualified more
precisely: if the potential is adequately measured, then there are no reasons for the Bass model to be over-optimistic.
But the actual and general problem with Bass approach is that it provides an elegant, attractive way of showing the
pattern of the diffusion but it does not provide in itself an estimate of the potential. This tends sometimes to be
forgotten by practitioners. There is nothing in the approach that prevents from being superficial with the estimate of
the potential.
2.3. Stated Preferences surveys
The third approach is based on Stated Preferences (SP) surveys. SP have first to be understood in opposition to
Revealed Preferences. It is indeed common to distinguish two sources of data in micro economics: Revealed
Preferences that rely on action actually performed by the subjects and Stated Preferences that are based on
intentions expressed by the subjects when facing hypothetical situations. While Revealed Preferences offer a number
of advantages, economists have accumulated experience in the latest decades that indicate that a well-designed SP
survey can supply useful information. Noticeably, they appeared suitable to generate larger datasets than RP within a
given budget constrain, which resulted in more restricted confidence intervals for the values of interest. They also
exhibited a good level of predictive accuracy (Massiani 2005). Moreover, in many situations where the good has not
been introduced yet in the market, no Revealed Preferences data are available, and SP offer an adequate (if not the
only) alternative.
SP have flourished into a number of varieties and with a number of labeling: Conjoint Analysis, Choice Based Conjoint,
Stated Preferences. Although these different names capture differences that are sometimes relevant, we will use,
unless specified explicitly, these different labels as synonym for the sake of this article. These labels share a common
conceptual setting: analyzing preferences of car purchasers based on a choice among hypothetical attribute
combinations.
Apart from the inexistence of Revealed Preferences data (which would not in itself be a merit of Stated Preferences),
SP offer a number of advantages compared with competing approaches for the sake of alternative fuel vehicles
diffusion forecast.
First, they provide information about the effect of non-monetary attributes. As long as an attribute is present in the
SP survey, information can be extracted on how it impacts the consumer choices. This appears to be of crucial
importance in the case of electric cars, in that they have some non-monetary features (range, refueling time, etc)
that make them very distinctive from conventional cars.
Second, SP are intrinsically calibrated to some data. This contrasts with TCO that, strictly defined, is not calibrated to
any behavioral information, and with Bass diffusion model which sometimes are, but sometimes are not, calibrated to
data.
Third, SP surveys results replicate consumers’ preferences in given market conditions. This can be an advantage in
situations where the decision maker is interested into consumer response in a given setting. For instance he is
interested by purchase intentions relating to a given national or regional market, or in a given period of time, or he
can be interested by the effect of certain specific attributes. One advantage of SP is that it is easy to tailor the data
collection process to the market conditions of interest.
18
Fourth, SP are intrinsically attribute responsive. This means that the forecasted choice probability of a given
alternative will always be dependent of the level of the attributes.
Limits of SP can be found in interrogations about some hypothetical distortions in the subjects’ answers to the
interviews.
2.4. Existing models
Another important body of literature relates to models. Table 1 indicates the most relevant models available to
forecast and evaluate the diffusion of electric vehicles. This type of approach can prominently be illustrated by the
U.S. project Transition toward Alternative Fuel Vehicles (TAFV: Greene 2001) and its successor (AVID, Santini and Vyas
2005a).
Model Country - Time frame
Type of model Market diffusion approach Observation
TAFV
(Greene 2001)
(and AVID),
(Santini and Vyas 2005)
USA Micro economic welfare maximization model
Discrete choice model. Coefficients derived from microeconomics and, partly, economic data
High level of resolution among technologies and fuel types
VISION
(Singh, Vyas et al. 2003)
(see also VISION CA)
USA-
until 2050
Spreadsheet model Exogenous market penetration assumption for different technologies
Diffusion pattern is strongly driven by numerous exogenous assumptions
Smart Garage (RMI) USA
2010-2030
Spreadsheet model Bass diffusion with exogeneous 50 % potential
Strong focus on time pattern of battery reload
AECOM
(AECOM Australia 2009)
Australia
Until 2040
Market penetration forecast
Synthetic Utility Function
CalCars
(Kavalec 1996)
California
1994-2015
Market and policy simulation model
Nested multinomial logit for ownership and technology choice based on RP and SP data
IPTS transport technologies model (Christidis, Hidalgo et al. 2003)
20 developped countries: up to 2020
System dynamics Weibull distribution based on costs, + Wood algorithm to take into account capacity constraints
Implemented in Vensim
Vector21
(Mock, Hülsebusch et al. 2009)
Germany
Until 2030
Extended TCO approach
TCO+wtp for “advanced vehicles”
Model includes 9 technologies and 900 customer types.
BEV diffusion is exogeneously limited (for instance to 50 % for small cars) to reflect range limitation
ASTRA
(IWW, TRT et al. 2000)
EU 27:
untill 2050
System dynamics model integrating macroeconomic transport and environment.
Discrete choice model. MNL Implemented in Vensim.
Discrete choice calibrated on diesel/gasoline competiton 1990-2006
19
Table&1:&Main&existing&models&for&the&forecast&and&evaluation&of&electric&car&diffusion&
Source: MMEM (2011)
2.5. Electric car evaluation
Apart from these models, which concentrate on the market penetration, the literature also proposed a number of
studies labeled as “cost benefit analysis” of electric vehicles. Most of the studies falling into this category actually use
this terminology improperly, at least to our view, as they consider the costs and benefits to car users only (Simpson
2006), or alternatively, the industry, or government agency (Kosub 2010), or sometimes omitting the externality
component of the COBA (Draper, Rodriguez et al. 2008). These studies negate the intrinsic holistic view of cost
benefit analysis that should consider costs and benefits to society as a whole.
Some studies, however, take a broader view on the topic. Kazimi investigates the effect of electric and alternative
fuel vehicles on air quality in the Los Angeles area and provides the monetary value of the related benefits (Kazimi
1997a; Kazimi 1997b). This analysis does, however, not compare benefits against costs. Funk and Rabl analyse the
private and social (= private + external) km costs of electric against gasoline and diesel vehicles in France (Funk and
Rabl 1999; Rabl 2002). Their findings indicate that while the total costs of EV are higher than diesel, they are not
generally lower than those of gasoline cars. Carlson and Johansonn-Stenman analyse the social costs and benefits of
the introduction of Hybrid technology among small cars in Swedish towns (Carlsson and Johansson-Stenman 2003).
Their main finding is that, due to the difference in taxation between electricity and fuel, the development of EV will
cost more to society than it will benefit through the reduced environmental externality. Such results can, however, be
found controversial. While their assumption of no burden cost of taxation is supported by solid arguments, their other
crucial assumption that reduced tax revenues is a cost to society is controversial and not aligned with the standards of
Cost Benefit Analysis as it constitutes a mere transfer between economic agents. Keefe, Griffin and Graham examined
the private as well as the total (private + externalities) costs and benefits of new fuels in the US (Keefe, Griffin et al.
2007). The scope of their research for the current policy process is however limited in that they consider hybrid
vehicles (parallel to “advanced diesel”, and E85) as the only electrified technology. Interestingly, their analysis aims
at integrating novel elements in a Cost Benefit Analysis framework like: the impact of reduced oil consumption on US
energy security or the rebound effect (increase in vehicle miles travelled when cheaper travelling technologies are
made available). Their finding is that “measured by NPV, the diesel is the most promising alternative” - a statement
that would seem provocative in a number of contexts (as, typically, in European ones) but whose scope is limited for
the current policy discussion due to the limited set of technologies considered and to the specificity of the Californian
context.
PriceWaterhouseCoopers also produced Costs Benefit Analysis of EV fleet deployment in Austria (PWC 2009). This
study takes into account changes in taxation, imports, energy consumption, and infrastructure investments (charging
stations, energy plants). While this study provides interesting insights (for instance showing that, in what can be
understood as a no policy scenario, the effect of EV diffusion on public budget is substantially neutral), it fails to
recognize the fact that COBA should treat as generally neutral transfers between agents and erroneously associate
costs and benefits to decrease/increase in general taxation.
In Australia, AECOM performed a simplified Cost Benefit Analysis of various policy scenarios in New South Wales
(AECOM 2009). Costs relate to purchase and operating costs of the vehicles, benefits relate to Green House Gas and
mostly, air pollution. The three scenario policies that are considered can strongly increase the net benefits of electric
vehicles diffusion. Such a result, however, constitutes a remote prospect as the Net Present Value of policies usually
becomes positive only in years after 2030.
As can be observed from the survey of previous studies, the number of available analysis is quite reduced when
considering the policy relevance of the issues and the number of countries which actually are considering Electric
20
Vehicles policy. Apart from the general need of keeping up with the pace of technological development and to
generate results in other contexts than the few investigated areas (Paris, Swedish towns, California, New South Wales,
Austria, Australia), the existing results need to be complemented with further investigations.
First, one needs to take into account the linkages of Electric Vehicles development with further economic impacts,
and with related (speculative) employment effects. Policy makers have a strong focus on the so-called “indirect
effects” and employment effects. In the absence of sound, microfounded analysis, the policy making process can
easily be occupied by fuzzy, policy driven, lobby produced figure which call for more rigorous approaches.
Second, there are some other issues on how “global” benefits like CO2 emissions should be accounted for in a Cost
Benefit Analysis with national scope.
Third, more fundamentally, few of these models (Aecom is an exception, Keefe as well but with the narrow
perspective of the costs and benefits to a public agency) are really policy valuation tools that would compare the
outcomes of policy scenarios with a properly defined reference scenario. Most of them concentrate on examining the
impact of an (often exogenous) EV diffusion. So these models evaluate the benefits of some (undefined) technology
development. Relevant is not what the cost/benefit of the apparition of a new technology could be, but how a policy
can improve welfare by influencing this development. What is needed is a tool that simulates the effects of policy
packages based on a set of incentives consistent with the policy currently considered by policy makers.
2.6. Review result
In the sections above, we have reviewed the existing models and results for the forecast of electric and alternative
fuel vehicles and the evaluation of related policies. We have found that a number of models are available. They
basically relate to three paradigms: TCO, SP surveys and Bass diffusions models with a limited number of additional
heterodox approaches.
We found that most of the models available for the diffusion of Electric Vehicles relate to the North American context
and/or provide limited insights into the relevant policy issues for European countries. Eventually we found that the
Cost Benefit Analysis of Electric Vehicle policy is still incipient as, to our best knowledge, notwithstanding the quality
and relevance of the works we have quoted in this article none of them constitute a satisfactory and comprehensive
evaluation framework for EV policies in European countries.
This picture suggests that the community of applied economists should dedicate efforts to the extension of existing
models focusing on a few features. Apart from the need to develop relevant and consistent evaluation tools, it is
possible to underline a number of modeling features that should be considered in order to render the diffusion
mechanisms, and correspondingly, the policy recommendations, more realistic.
First, there is a general need to develop adequate modeling and evaluation tools for the European context. Second,
we find that a stronger focus should be made in the model development about diffusion mechanisms. In many of the
existing models, diffusion is exogenous, which makes it virtually impossible to make policy assessment. In other
models, we find that the adequacy of the behavioral parameters is questionable: whether it is based on a given SP
survey that can prove very idiosyncratic, or whether it is calibrated on a very limited set of data (like diesel/gasoline
market shares). Additionally, one should consider how the diffusion theory insights should be integrated together with
discrete choice models. There is a wide discrepancy between the meaning that marketing science gives to SP based
market shares estimates and the meaning given to these estimates by transport scientists. How these two diverging
approaches should be reconciled is still on the agenda of transport modelers and marketing scientists.
Third, one should consider that most of the existing models present limited interactions with the energy sector, while
this sector will certainly be impacted by the development of EV and reversely some policy measures will probably be
implemented through the energy sector (consider refueling stations). Similarly to energy sector, we also reckon that
21
more attention should be dedicated to car industry and to the CO2 emissions standard that this industry will have to
face due to EU/443 regulation. Such a change in the regulatory setting is felt to be a major change in the car market
and may constitute a strong input to EV diffusion. In this context it is fair to state that the modeling of EV diffusion
should explicitly take into account the effects of this regulation on the car industry and indirectly on car market.
It is our view that, taking into account these indications, evaluation models can become a relevant tool for the
definition of EV development policies in European countries.
22
3. Car Market in MMEM
3.1. Conceptual framework for the representation of the car market
In this section we present the different dimensions that define the car market modeling. This refers to the definition
of the different markets, segments, and technologies.
We first provide a short overview of the general choice modeling.
EMOB core component is a market simulation module that is based on a Discrete Choice Modeling to forecast the
evolution of different automotive technologies on the German market. It represents the choice among 9 competing
technologies (Gasoline, Diesel, Hybrid, Biofuels, LPG-CNG, BEV, Range Extender, Plug-in Hybrid, Fuel Cell). The car
market is divided into different submarkets (privately owned household cars, rental cars, car purchased by resellers,
Dienstwagen i.e. cars provided by companies to their employees as a fringe benefit, corporate fleet including
company cars, and public procurement), which are characterized by differing purchase mechanisms.
In addition to the differentiated characterization of consumer groups, the model takes into account the different
vehicle segments of the market, corresponding to different vehicle sizes, with a level of decomposition that is fairly
larger than in other existing models, and is based on the categorization of the Kraftfahrtbundesamt (KBA) in use in the
German administration. It includes 11 categories (Minis, Kleinwagen (small cars), Kompaktwagen, Mittelklasse, Obere
Mittelklasse, Oberklasse, Geländewagen (Sports Utilities Vehicles), Sportwagen, Minivan, Großraumvan (people-
carrier), and Utilities (light freight vehicles)). Different to most of the existing models, our model endogenises
segment choices. This means that segment choice is responsive to changes in the consumer choice environment.
Practically this allows consumer to react to, for instance, a change in vehicles costs by shifting segment, rather than
technology, a feature that certainly is needed to make realistic projections of market responses to policies.
The model is “dynamic”, i.e., the market shares of respective technologies and segments are a function of the time-
dependent value of car attributes.
The model elaborates on a meta-analysis of Stated Preference surveys and constructs a synthetic utility function
based on willingness-to-pay (WTP) and elasticities found in the literature. The model also contains a “diffusion”
module, which uses the Discrete Choice model as input data (to be understood as “potential market shares”) and
computes adjusted market shares based on a Bass-like diffusion model (Bass 1969).
The model can be run for a reference scenario that represents the most likely scenario and conceptually reflects
measures that are already approved. It can also be run for a variety of policy scenarios that activate a series of policy
measures (purchase incentive, fuel taxation, etc).
3.2. Aggregated car demand
In this section we present how the aggregated car demand (number of vehicles sold on the German market) are
estimated for future years.
3.2.1. Segment choice by size
Several studies (for example Cambridge econometrics 2008, Vance and Mehlin 2009) indicate that vehicle size plays an
important role when it comes to vehicle choice. It can be seen as an indicator for luxury and comfort attributes
attached to a car as well as an indicator for utility (luggage space, number of passengers, etc.). The segmentation
used in MMEM also suggests that those attributes are widely shared by every car in the same segment.
23
MMEM also reflects this fact by means of a willingness to pay assigned to segment vehicle size. The latter can be
concluded upon by the wheelbase. The willingness to pay for vehicle size has been taken from (Cambridge
econometrics 2008), normalizing it to purchase price to make it applicable in the synthetic utility function. The
resulting attached value to size of the wheelbase and, indicated by that, size of the car is 3.51€/mm.
Average wheelbase sizes used in the model were derived from the MMEM pool of cars in the gasoline segment and are,
as mentioned above, assumed to remain unchanged for any selection of technology (see Table 2).
Segment Wheelbase
Mini 2117 mm
Kleinwagen 2491 mm
Kompaktklasse 2630 mm
Mittelklasse 2759 mm
Obere Mittelklasse 2895 mm
Oberklasse 3053 mm
Geländewagen 2738 mm
Sportwagen 2430 mm
Minivans 2644 mm
Großraumvans 2691 mm
Utilities 2682 mm
Table&2:&Wheelbase&by&segments&
Source: MMEM (2011)
3.2.2. Choice probability generation
To further model consumer choices a nested logit model is applied. Logit models are state of the art choice
probability generation approaches. The idea is to evaluate how differences in utilities lead to different choice
probabilities.
Logit models belong to the generalized extreme value (GEV) family. All members of this class of models share the
common trait of the unobserved part of utility for all alternatives being jointly distributed as a generalized extreme
value. This allows for correlation over alternatives (Train 2009).
The type of GEV model applied in MMEM, the nested logit model, is a very widespread approach being used in a wide
array of different fields including energy, transport, housing, telecommunications and others. Prominent examples are
also given in (Train 2009).
In MMEM customers decide not only on propulsion technologies but also on their preferred segment. These two layers
of decision need to be reflected in the choice probability generation. Therefore a so called three-level-nested logit
model is applied according to the following set of formulas:
24
Top-level: !" =exp ()"∗+")
∑ exp (). ∗+. )/.=1
with +" = 12 3∑ 456 789("))"
∗ :9(");<(")9=1 =
Middle-level: !>|" =exp @
8>("))"
∗:> (")A
∑ exp <(")9=1 @
89("))"
∗:9(")A with :>(") = 12 B∑ 456 @
CℎE>(")F
8> (")AG(")
ℎ=1 H
Bottom-level: !1|>(") =456 I
C18>(")
J
∑ 456 ICℎ
8>(")JG(>("))
ℎ=1
K
In MMEM k, m, l label segments, technologies and individual alternatives, the corresponding capital letters label the
total number of items in the respective category. Top level corresponds to segment choice while middle level is
related to technology choice. On bottom level individual alternatives can be found.
I and J are the inclusive values, 0<λ,σ<1 ∀ m,k are their corresponding inclusive value parameters (also called nesting
parameters). The latter determine the strength of correlation amongst nested alternatives. The choice of these
parameters is essential to choice probability generation in those types of models and will be discussed in detail in the
next section.
The combined probability is calculated as:
Comb. prob.: P = P!*P!|!*P!|!(!)
The GEV family also includes many more choice probability generation models. There are more simplistic approaches
like the multinominal logit approach or the simple nested logit as well as more sophisticated structures like the cross-
nested-logit (CNL). While the latter seems the obvious choice in terms of a non-hierarchical choice process (in a CNL
every alternative can belong to several nests at the same time, thus removing the hierarchy imposed in a multi-level
nested logit), it is rather difficult to come up with the necessary parameters. Literature suggests an even split in
importance of the two choices in many cases and nesting parameters can be taken from nested logit estimations.
However, there is no empirical evidence for such a choice to be imperative. While it is suggested as a long term target
for MMEM to switch to a cross-nested-logit structure (de Jong 2011), a three-level nested logit is a justifiable
simplification for now. Also the computational effort with a CNL will be a lot higher.
3.2.3. Nesting structure – choice of nesting parameters
The nesting parameters here are chosen to reflect a similarity in size and usage of the vehicles on the segment level
and a similarity in terms of propulsion technology and establishment in the market on the technology choice level.
Smaller parameters indicate more similarity (correlation) between the elements of the contained nests. This results in
alternatives in highly correlated nests taking market shares primarily from alternatives within their own nest.
A nested logit model allows correlation between the error terms of alternatives that belong to a common nest,
whereas the unobserved components of alternatives from different nest are uncorrelated. This leads to a higher
degree of substitution between the alternatives within the same nest. Each nest has a nesting coefficient, which has
to be between 0 and 1 for global consistency with random utility maximization. If the value is 1, the model becomes
the standard (uncorrelated) multinomial logit MNL, values closer to zero give a higher correlation between the
alternatives in the nest.
25
Different nesting structures are possible within a nested logit model. Nesting can also be used to that express that a
segment-technology combination and another combination with the same segment are closer substitutes than
combinations with different segments (segment-technology nesting).
Conversely, a nested logit can represent that a segment-technology combination and another combination with the
same technology are closer substitutes than combinations with different technologies (reverse segment-technology
nesting).
For the choice of nesting parameters MMEM relies on the expertise of director of Significance (Netherlands) Prof. Dr.
Gerard de Jong. In his memo to ESMT(de Jong 2011) he discusses the prototype nesting structure initially suggested by
ESMT that was based on aggregated German car market data (Vance and Mehlin 2009):
The present ESMT note uses Vance and Mehlin (2009) as a source for the nesting parameters. However, these coefficients were estimated on aggregate German car market data. In an aggregate context, the scale parameters can be quite different from a disaggregate setting (since the unobserved factors might be rather different, and consequently their variance). This makes Vance and Mehlin a less ideal source for the determination of the coefficients in a model that is applied at the micro-level. The parameters in Hess et al. (2009) are based on a different car market (US, where available car types are rather different), but it has been estimated at the disaggregate level. Another difference is that Hess et al. focus on whether technology should be nested above segment, the other way around or both at the same time, whereas ESMT focuses on nesting within segment choice and within technology choice. Considering all this, Vance and Mehlin (2009) will be a more relevant source for the parameters of the ESMT structure than Hess et al, (2009).
As a result of this discussion Dr. de Jong concludes the nesting structure displayed in Figure 3 being the best modeling
approach.
@,91;(&A6&""!"&+(8'(/&.09,'&8';1)'1;(&
Source: MMEM (2011)
26
3.2.4. Technology and segment choice modeling
In this section we present the method used for market share calculation. Our approach is based on the well-
established Discrete Choice Model (DCM) framework and more specifically on the Random Utility Maximization (RUM)
paradigm. DCM is a micro-founded method that relates purchase choice probabilities to the product’s, in our case
cars’, attributes. To put it simply: Discrete Choice Models combine the attribute (performance, price, range, etc…) of
a car with the “weight” that a consumer assigns to each of these attributes, and derives, based on this combination,
the probability that the consumer chooses a certain alternative. This combination process takes the form of cardinal
“utility” functions. These functions have a systematic part, which is deterministically determined by attributes and
“weights”, and a stochastic part that reflects the fact that in similar choice conditions there could be various choice
outcomes based, for instance, on differences among consumers’ tastes.
The “weights” or piecewise utilities assigned to various alternatives are usually derived on Conjoint Analysis (or Stated
Preferences) surveys, which collect people’s preferences about one rather than another car alternative, with its
features. Rather than performing such a survey and using its results for our forecast, we decided to use a more
consolidated approach, which is to rely on a meta-analysis based “Synthetic Utility Function” that elaborates on a set
of surveys and results documented in the scientific and economic literature. Advantages of this meta-analysis
approach, in our view, are:
! It provides results that are less dependent of specific context and elicitation process of given SP survey.
! It allows for introduction in the choice model of attributes that are not present in a given SP survey,
thus expanding the set of variables present in our model and improving its realism.
! It allows to use information available through sources of data others than SP surveys data (hedonic
pricing for instance) in order to check, validate and filter available data.
When selecting the Discrete Choice Modeling approach, we also have to address another issue: DCM comes with a
variety of flavors, and one has to consider which one is appropriate for our topic. We elected the Nested Logit, as the
adequate one. This relies to the fact that Nested Logit realistically represents “correlations among alternatives”.
Analytically, Nested Logit formulation derives from the existence of statistical correlation among the stochastic
component of the utilities of different alternatives. Literally this means that, net of attributes included in the utility
function, there may be “something in common” among two alternatives. For instance a Diesel car may be found closer
to a Gasoline car than to a Fuel Cell car, or a mini car can be found closer to a compact-class car than to a Oberklasse
car. As a result, when computing market share, the model replicates these correlation by creating increased
substitution between vehicles of a given nest. This represents the fact that when, say, Diesel car costs increases,
Gasoline car is a “closer” substitute than Battery Electric Vehicles, or other alternatives.
Once Nested Logit is elected as the adequate DCM formulation, and meta-analysis is chosen as the method to inform
consumers’ trade-off, the remaining task is just the fundamental one: quantifying the weight of the various attributes
in consumer preferences. This is done by finding the right quantification for the willingness-to-pay for the different
car attributes. Successively one needs to convert these willingness-to-pay into utility functions. Eventually, one needs
quantify the correlation in the nesting structure. In the next sections, we review in turn these different questions.
27
3.2.5. Willingness to pay for car attributes in the household purchase model
The analysis we have made is based on a set of SP surveys performed in different contexts over the latest years. Table
4 lists the different surveys that we have identified and analyzed for our purpose.
Academic (peer reviewed) publications: Other academic papers:
(Beggs and Cardell 1980)
Train 1980
(Beggs, Cardell et al. 1981)
(Calfee 1985)
(Bunch, Bradley et al. 1993)
(Ewing and Sariloglu 1998)
(Tompkins, Bunch et al. 1998)
(Brownstone and Train 1999)
(Brownstone, Bunch et al. 2000)
(Dagsvik, Wennemo et al. 2002)
(Batley, Toner et al. 2004)
(Zito and Salerno 2004)
(Potoglou and Kanaroglou 2007)
(Ahn, Jeong et al. 2008)
(Caulfield, Farrell et al. 2010)
(Mabit and Fosgerau 2011)
Hidrue and Parsons 2011
(Golob, Kitamura et al. 1991)
(Knight 2001)
(Batley and Toner 2003)
(Adler, Wargelin et al. 2003)
Horsky and Nelson 2004
(Knockaert 2005)
(Kuwano, Zhang et al. 2005)
(Potoglou and Kanaroglou 2007)
(Högberg 2006)
(Achtnicht 2008)
Ziegler 2009
(Hess, Fowler et al. 2009)
Achtnicht, Bühler et al. 2009
(Dagsvik and Liu 2009)
Applied forecasting:
(IMUG 2010)
(Öko-Institut and ISOE 2011)
Table&4:&Set&of&SP&surveys&used&in&MMEM&
Source: MMEM (2011)
Analyzing these different surveys, it was possible to derive a WTP for a set of attributes. These WTP data
(homogenized in 2009 € values) are reproduced on
Table 5 for a set of variables and studies that were found most central for our analysis.
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miss
ions
52 (f)
-10-11
8 (me
an
coeff
. valu
e)€/
g/km
Pollu
tion/E
miss
ions
(€/%
)-71
,2 (h)
-330
-90 (q
)-80
(g)
€/% of
vehic
le em
ission
sSe
rvice
Stati
ons
€/% of
stati
ons
6373
166
3138
5 (me
an
Rang
eRa
nge
(€/
100k
m)42
35 (t)
1714
9813
2099
1467
2426
2072
€/100
kmPe
rform
ance
Acce
lerati
on (0
-30 m
ph)
(€/se
c)-15
09 (p
)23
5 (o,f
)€/s
ec.
(0-10
0kmh
)
(€
/sec)
-614
€/sec
.To
p spe
ed
(€
/km/h)
997
393
63-28
€/ km
/hMo
tor po
wer
(€
/hp)
157
7015
,214
5€/h
pTe
chno
logy
ICE
Gene
ric IC
E
(€)
refere
nce
refere
nce
refere
nce
refere
nce (
k)ref
erenc
eGa
solin
e
(€)
262
refere
nce
refere
nce
-1356
5€
Dies
el ve
hicle
(€
)ref
erenc
e43
07-45
2ref
erenc
e€
Hybr
id
(€)
-4047
3341
3274
159
6123
-613
-1253
249
18€
(€)
-1028
9 (j)
€Ga
z
(€)
-6111
(n,l)
5588
(n) 9
713 (
m)22
73 (l)
-7636
(l)-14
08-17
274
3087
(LPG
)€Bi
o fue
lsBi
ofuel
vehic
le
(€)
-1087
442
22 (i)
-1839
-2064
5€
Bio-d
iesel
(€)
€Hy
drog
enFu
elcell
vehic
le
(€)
-5139
1033
6-13
98-13
09-18
339
€Ele
ctric
Electr
ic ve
hicle
(€)
-1367
2-16
511
2874
09-37
50-22
66-28
258
2329
€Alt
ernati
ve fu
elsAlt
ernati
ve Fu
el
(€
)-30
86€
Refue
ling c
ondit
ions
If refu
el loc
ation
at ho
me
(€
)-86
76 (u
)€
Time f
or a
full re
fuel
(€/mn
)-11
6€/m
in.
Refue
l onc
e eve
ry 2 d
ays
(€)
3474
(w)
€Re
fuel o
nce e
very
4 day
s
(€)
6016
(w)
€Re
fuel o
nce p
er we
ek
(€)
8617
(w)
€Tr
unk v
olume
92 (w
)€/%
28
Table&5:&WTP&in&MMEM&
Source: MMEM (2011)
(a) Computed for income = 78000 US$ (b) Computed at average sample income 38000 US$ (c) Mabit & Fosgerau: maintenance, fuel expenses, taxes (d) Weighted average of six models based on six different age-gender groupings (e) Mabit & Fosgerau. 2010: if extra service and repairs other than maintenance is included in annual cost (f) Non significant coefficient (g) % of diesel/gazoline car (h) "Tailpipe emissions as fraction of comparable 1995 new gas vehicle". Converted in €/% in the table (i) Biodiesel (j) California (k) In Mabit & Fosgerau (2010) the fuel-specific constants capture pollution effects for the different alternatives.(l) at mean range (m) compared with petrol stations (n) Compared to refueling every day (o) 0-60 mph. (p) 0-30 mph. (q) wtp for a reduction in a 1-10 range of emissions is converted into % assuming 1-10 range represents 0-100 %. *** Knockaert (2005) reports "percentages", understood as "fractions", so values calculated from coefficients reported in Knockaert (2005) are divided by 100 (r) model 4. MXL merging two databases. (s) Table 2 :" MNL based on full data set following cleaning".
Achtn
icht,
2009
, 201
0Br
owns
tone,
2000 (b)
Dags
vik,
2002
(d)
Axse
n, 20
09
(a)
Batle
y et
al., 2
003
(s)
Batle
y et
al., 2
004
(r)
Mabit
&
Fosg
erau,
2010
Knoc
kaert
, 20
05Zie
gler,
2010
Av
erage
d ov
er 4
Achtn
icht,
2009
(RPL
mo
del)
Dags
wick
(Fe
males
, ag
ed 30
-49)
Unit (
Euro
s are
in 20
09
price
s)
Categ
ory
Attrib
uteGe
rman
yCa
liforn
ia No
rway
Cana
da+
Califo
rnia
UKUK
Denm
arkBe
lgium
Germ
any
Germ
any
Norw
ay
Varia
ble co
stsFu
el co
sts
(€/ €/1
00km
)-19
38-93
4-99
2-17
9-35
03 (m
ean
coeff
. valu
e)(€/
€/10
0km)
(€/ €/
yr)-4,
9(€/
€/yr)
(€/
l/100
km)
-1049
-823
(€/ l/1
00km
)An
nual
cost
(€/ €/
yr)-4,
2 (c)
-4,7
(€/ €/
yr)Ru
nning
Cos
ts
(€/ €/1
00km
)-75
8-24
4(€/
€/10
0km)
Servi
ce
(€
)23
36 (e
)(€)
Emiss
ions
CO2 e
miss
ions
52 (f)
-10-11
8 (me
an
coeff
. valu
e)€/
g/km
Pollu
tion/E
miss
ions
(€/%
)-71
,2 (h)
-330
-90 (q
)-80
(g)
€/% of
vehic
le em
ission
sSe
rvice
Stati
ons
€/% of
stati
ons
6373
166
3138
5 (me
an
Rang
eRa
nge
(€/
100k
m)42
35 (t)
1714
9813
2099
1467
2426
2072
€/100
kmPe
rform
ance
Acce
lerati
on (0
-30 m
ph)
(€/se
c)-15
09 (p
)23
5 (o,f
)€/s
ec.
(0-10
0kmh
)
(€
/sec)
-614
€/sec
.To
p spe
ed
(€
/km/h)
997
393
63-28
€/ km
/hMo
tor po
wer
(€
/hp)
157
7015
,214
5€/h
pTe
chno
logy
ICE
Gene
ric IC
E
(€)
refere
nce
refere
nce
refere
nce
refere
nce (
k)ref
erenc
eGa
solin
e
(€)
262
refere
nce
refere
nce
-1356
5€
Dies
el ve
hicle
(€
)ref
erenc
e43
07-45
2ref
erenc
e€
Hybr
id
(€)
-4047
3341
3274
159
6123
-613
-1253
249
18€
(€)
-1028
9 (j)
€Ga
z
(€)
-6111
(n,l)
5588
(n) 9
713 (
m)22
73 (l)
-7636
(l)-14
08-17
274
3087
(LPG
)€Bi
o fue
lsBi
ofuel
vehic
le
(€)
-1087
442
22 (i)
-1839
-2064
5€
Bio-d
iesel
(€)
€Hy
drog
enFu
elcell
vehic
le
(€)
-5139
1033
6-13
98-13
09-18
339
€Ele
ctric
Electr
ic ve
hicle
(€)
-1367
2-16
511
2874
09-37
50-22
66-28
258
2329
€Alt
ernati
ve fu
elsAlt
ernati
ve Fu
el
(€
)-30
86€
Refue
ling c
ondit
ions
If refu
el loc
ation
at ho
me
(€
)-86
76 (u
)€
Time f
or a
full re
fuel
(€/mn
)-11
6€/m
in.
Refue
l onc
e eve
ry 2 d
ays
(€)
3474
(w)
€Re
fuel o
nce e
very
4 day
s
(€)
6016
(w)
€Re
fuel o
nce p
er we
ek
(€)
8617
(w)
€Tr
unk v
olume
92 (w
)€/%
29
Based on these data it is possible to propose a WTP for the different attributes that influence the purchasing process.
We review these different attributes.
Autonomy
Autonomy relates to range and refueling network. These two attributes probably interact (the less refueling network
is dense, the more it is important to have a large range), but for simplification purpose we expose them
independently.
Range
The marginal value of range is expected to be high for small range and become smaller when the range increases,
until it becomes negligible when the range has reached a level comparable with conventional cars. The aspect of such
a relationship is displayed on Figure 6 that represents both the marginal and cumulated willingness-to-pay for range.
For simplification purpose, considering this is smaller than the minimal range that we have to consider in our model,
we will consider only range higher than 50 km.
&
Figure&6:&WTP&for&range&
Source: MMEM (2011)
Paradoxically, range is absent of numerous studies and is supposed to be a constant marginal value in most of the
existing sources (exceptions are: Brownstone and Train 1999; Dagsvik, Wennemo et al. 2002). Range is sometimes
interacted with socio-economic feature (Mabit and Fosgerau 2011 interact age with range). Other studies (Greene
2001) consider that range disutility can be approached through the extra refueling time needed, a statement that may
be reductive compared with the true inconvenience of range limitation.
In this context, providing a reliable estimate of willingness-to-pay for range is a challenge. It is however consistent
with available data to assume an average WTP in the range 20 – 160€/ km. This assumption can be further refined
taking into account the curvature of the WTP. Information on such curvatures can be found in IMUG 2010, Brownstone,
Bunch et al. 2000 and Öko-Institut and ISOE 2011. Figure 7 provides an illustration of such values. This figure also
!
Range km
Willingness to pay for a marginal increase in range
Willingness to pay for a given range
50 km
30
includes a regression of marginal willingness-to-pay as a function of range, based on IMUG and ISOE (average of the
three car segments) data. We concentrate on these latest data in order to make a recommendation closer to
preferences prevailing in nowadays European markets.
@,91;(&G6&"-;9,+-.&H,..,+9+(88I'0IJ-?&:0;&;-+9(&4-8(/&0+&<-;,018&8'1/,(8&
Source: MMEM (2011)
Based on the IMUG and ISOE (average of three car segments) data, marginal willingness-to-pay for range can be
estimated as (range in km):
Marginal WTP = 27951 x range-1,27
So the “total” willingness-to-pay can be estimated as
WTP = -103522 x (range)-0,27
This estimate could be further improved by taking into account the change in WTP for ranges between 400 and 1000
km, values that are usually not considered in the available literature. Such an extension of this function may prove
non-futile as some technologies typically have a range that can go up to 1000 km. It however appears difficult to
perform such an extension based on existing results, which implies that such an improvement to the model can
probably be performed only when more SP surveys would be available on that precise issue.
Refueling facility
The effect of refueling stations should take into account differences between the different technologies available.
1. Ultrafast reload (10 minutes for 80%)
2. Fast reload (2 hours for a full reload)
3. Normal reload (6-10 hours for a full reload)
These latest in turn differentiate in home plugs, office plugs and street plugs.
Most of the results available in SP surveys do not properly specify what type of refueling facility is considered. In
those situations, it is likely that respondents will consider refueling conditions that are similar to the ones they
experience with conventional fuel vehicles. This means that the willingness-to-pay for an increase of refueling facility
31
should be interpreted as willingness-to-pay for ultra-fast charging. Correspondingly, the Willingness to Pay for other
refueling technologies should be much lower.
Willingness to pay for Ultra-Fast charging
In Achtnicht, Bühler et al. 2009 and Ziegler 2009, the marginal utility of refueling is a constant and their models
exhibit a very strong effect of these variables with a Willingness to Pay ranging from 200 to 300 €/% (% relates to the
share of refueling stations that provide a given fuel). However, one limitation of these results is that they rely on a
constant marginal WTP while it is more than likely that the marginal utility of refueling stations will be decreasing.
For this reason, we prefer the approach by Greene 2001, where the utility of refueling stations can be represented as
an exponential function:
bsufc Ce)(V =s
With:
Vufc piecewise utility associated with the ultra-fast charging network,
s share (fraction) of refueling stations that offer fuel,
b and C behavioral parameters.
The parameters b and C can be identified by assuming values for the disutility of (lack of) refueling stations for s = 0
and for another appropriate value of s. Assuming a penalty of 5000 € for a 0% refueling stations (y1=-5000), and 1000 €
for 20% refueling (y2=-1000, x2=0,20) we obtain the following equations that can be used in the synthetic utility
function.
( ) ( )( ).s0,1/0.2ln
cufc e5000V β=
Car performance
Car performance can be expressed in terms of horsepower (HP) or acceleration time or maximum speed. Considering
that these various elements are linked, a parsimonious model shall concentrate on only one of these attributes. HP
was elected as the relevant attribute due to the availability of HP data across a number of surveys. Available WTP for
HP range from 142 €/HP (Achtnicht 2010) to 55€/HP (Axsen, Mountain et al. 2009).
Additionally, we collected information on market value of an additional HP based on a set of vehicles. Example of
such data, as provided by Table 8, indicates a market value of HP slightly larger than 50€/HP.
Vehicle Motor HP public price
(€)
Cost
€/HP
Citroen C4 coupé seduction 1,4 16 V 88.0 16500
1,6 16 V 108.8 17500 50 €
VW Golf TSI Comfortline 1.2 TSI 104 20375
1.4 TSI 121 21250 51,4744 €
BMW 3 edition lifestyle 1,6 Diesel 115,6 29500
1,8 Diesel 142,8 31350 68 €
Table&8:&market&value&of&extra&Horsepower&
Source: MMEM (2011)
32
Based on these consideration we would not recommend to use a WTP that is lower or equal to the 50€/HP revealed by
market conditions.
Environmental features (emissions)
Moving on to our next attribute we consider how much consumer would value the emissions of vehicles. Results of
existing surveys are not highly conclusive and sometimes counterintuitive (see for instance the positive sign of the
coefficients associated with CO2 emissions in Achtnicht 2010, while Ziegler 2009 obtains negative coefficients on the
same data). A reason for such situation could be that consumers are little informed and potentially little concerned
about the CO2 emissions of their cars. As a consequence, it is assumed reasonable to consider a 0 WTP for CO2
emissions. While this figure may be contrary to the expectations that consumer value “green car” better than other
cars, we find little support for such an assumption in the surveyed Conjoint Analysis.
Variable costs
Variable costs relate to costs that are spread across the lifetime of the vehicles. They may or may not be related to
kilometrage. They are namely fuel costs, road tax, annual parking charges. They are dealt with consistently in our
modeling approach. We present the computation rule that we used for fuel costs and for other variable costs items.
Fuel costs
Relating to variable costs we exploit a remarkable pattern that appears across various SP surveys (Axsen, Mountain et
al. 2009, Mabit and Fosgerau 2011, Knockaert 2005) with converging results in that operating costs are accounted for
4,2 to 4,9 years of expenses. This number, significantly lower than the lifetime of vehicles, expresses the strong
discounting effect taking place in operating costs valuation. An average value of 4,5 years of expenses, an assumption
that makes (perceived) fuel cost intrinsically dependent on mileage, provides a reasonable guidelines to estimate the
impact of fuel cost on purchase behavior and is used in the Synthetic Utility Function.
Road tax costs
Road tax payments are a significant part of the cost connected to owning and operating a vehicle. It should thus be
included in the purchase decision. Especially since road tax exemptions for alternative propulsion technologies play an
important role in promoting new technologies. We follow a similar approach than with fuel costs, relying on 4,5 years
rule. Thus, prospective buyers would factor in the net present value of future road tax payments. These depend on
the average holding time and the annual tax rate of the car type.
Parking charges (and their possible exemptions)
Parking charges exemptions could have an impact on purchasers’ decisions, especially in a context where it
constitutes probably the main non-monetary incentive for alternative vehicle policies.
33
Based on our own elaboration made from the travel survey ‘Mobilität in Deutschland’, the average car owner has
annual costs for parking (and searching for parking spaces) of 78! per year. Figure 9 provides an overview of the
underlying data used to calculate the monetary equivalent of parking costs for the average user.
@,91;(&N6&D-;L,+9&(7J(+/,'1;(&-+/&)08'&4?&-;(-&
Source: MID 2008
Similar to other maintenance costs the user considers the discounted cumulative parking cost savings in his buying
decision. In the reference scenario, this cost is added to all alternative which makes it effectless on the choice. This
however provides the possibility to model targeted policies that would exempt only certain categories from parking
fees.
Costs of private parking hire
Parking hiring costs are introduced in the model as a way to replicate the different choice context of car purchasers
which have or do not have a garage. Household car buyers are segmented into those who have private parking
available versus those without a private parking (so-called street parkers). This makes it possible to estimate separate
buying probabilities for Electric Vehicles depending on availability or absence of private parking and charging
opportunities for EVs. This allows estimating how infrastructure policy could affect choice behaviors of street parkers.
34
@,91;(&5O6&$;(-L/0H+&0:&)-;&0H+(;8*,J&4?&-<-,.-4,.,'?&0:&J-;L,+9&
Source: MMEM (2011)
We use ‘Mobilität in Deutschland 2008’ data to assign parking costs to various geographic groups of car users.
According to this survey circa 30% of car owners do not have available a private parking space. It is this group that
would have to hire parking and charging infrastructure in case of owning an electric vehicle that requires charging via
the electricity grid (see Figure 11).
3.2.6. Household segmentation
We have segmented household car purchasers into two dist inctive sub-groups: those with and those without a private parking space. Both groups have the same decision-making rationale reflected in an identical synthetic uti l i ty function with the exception of a specif ic attr ibute that reflects the penalty of not having a garage for the relevant population. Car buyers without a private parking consider the costs of having to rent private parking in order to be able to park and charge electric vehicles. This is factored in the costs of renting a parking space ( including a fee to cover the provision of charging equipment). The cost of parking hire has thus two components: The actual parking hire costs and the cost of the charging equipment. A prospective buyer would consider the discounted value of these future costs. Table 11 shows the cost assumptions obtained from our research of parking hire costs. &
Type of parking space Annual costs
Car park 766 !
Private street parking 517 !
Estimated wil l ingness to pay for parking in front of the property
508 !
3-4.(&556&#<(;<,(H&-8812(/&J-;L,+9&*,;(&)08'8&
Source: MMEM (2011) using data from Mobilität in Deutschland 2008
35
We also estimate the addit ional cost of hir ing charging equipment based on a simple net present value model for charging equipment. This annual value is discounted following the general 4,5 years rule that reflects consumer intertemporal preferences. As these costs are substantial over the assumed usage t ime, it should be of l i tt le surprise that the group without parking opportunity is far less l ikely to buy electric cars than buyers who have already access to private parking. Purchase costs and other fixed costs
Expected wall box costs
In addition to the actual car purchase costs, buyers of electric vehicles also need to have charging equipment
available. We argue that the informed buyer will consider the additional cost in her buying decision. As it constitutes
addition to the purchase price, it is exactly treated like this in the SUF. Specifically; prospective buyers value the cost
of charging equipment (consisting of equipment costs and cost of installing the device) the same as purchase price.
Battery replacement
This addresses the fact that battery life time may be limited and car buyers would factor in the expected cost of
future battery replacement. The expected cost of a battery replacement relies on assumptions regarding the life time
of batteries as well as the default probability.
Year Assumed battery life time
01.01.2011 7
01.01.2020 10
01.01.2022 12
01.01.2050 15
Table&12:&Assumed&battery&life&time&
Source: MMEM
The expected battery replacement costs depends on the assumed battery life time (tbattery), car holding time
(tusage) and the expected battery costs in the replacement year (cbattery) as shown in the formula below.
! !!"#$%&" = !!"#$% − !!"##$%& !!!"##$%&!!"##$%&
# !
3.2.7. Price elasticity
Once WTP have been defined, one needs an additional piece of information about how much car purchasers are
affected by differences in the “utility” of a given alternative. Given the stochastic nature of the model, the fact that
a given alternative has higher “utility” than another does not imply that it will be chosen. A model can be fully
specified only once one has determined how sharply people shift from one alternative to another when the first
alternative becomes preferable (based on the attributes included in the model). The usual way to determine how
“strong” this adaptation should be is too look at real world data measuring elasticity of car purchase to price changes.
This topic is dealt in more in detail in section 9 and 12 which provides the value used for our model.
36
4. Diffusion approach in MMEM
Empirical data show that buyers only gradually adopt innovations. Factors such as lack of experience, low visibility,
word of mouth, branch density, etc. make that the full market potential of a new product or technology will only be
reached after some time.
The consumer choice model within the MMEM produces market shares that reflect the full market potential of a
propulsion technology based on its characteristics such as purchase price, fuel cost, maintenance cost and others.
However, new vehicle technologies such as battery electric vehicles or range extenders are unlikely to reach their
market potential immediately after entering the market. Therefore, not considering diffusion effects is likely to
produce overestimations of market shares of emerging technologies at the beginning of the forecasting period. Hence,
in order to consider the adoption time of new technologies, a suitable diffusion approach is required.
Given the fact that dealing with electric mobility implies dealing with new technologies we expect the reaction of
consumers to be more biased towards refraining from buying electrical cars than the analysis of stated preference
surveys indicates. Analogous behavior can be observed in all kinds of markets when new technologies are introduced.
To adequately simulate the expected diffusion of new technologies in the market we apply a modification of a
classical Bass model. Modifications are necessary to achieve conformity with our very general market modeling
approach. We shall explain those modifications in detail and also give an overview on the mechanism of impact of
policy measures on the Bass diffusion. Additionally we provide a description of the meta-analysis we carried out to
access the Bass diffusion parameters incorporated in our model.
4.1. Theoretical background of diffusion theories
Electric vehicles, hybrid vehicles, biofuel vehicles and, to some extent, LPG and CNG propelled vehicles can be
considered as new propulsion technologies which could potentially substitute established internal combustion engine
technologies (i.e. gasoline and diesel powered vehicles). The adoption of new technologies often follows an S-shaped
curve as cumulated purchases of product innovations can be characterized by three different growth phases: A slow
take-up phase, followed by a phase of more rapid growth as the technology becomes widespread and, finally, slowing
growth when the ‘not so new’ technology approaches saturation. Diffusion theories try to explain the actual shape of
diffusion curves – i.e. the speed and the shape of cumulative adoption of an innovation among a set of prospective
buyers.
A widely used approach in marketing to explain the diffusion of innovations is the so-called Bass Model (Bass 1969).
The Bass Model assumes that potential buyers of an innovation can be divided into two groups:
! Innovators: People that buy the product first and are influenced only by ‘external communication’ e.g.
mass media
! Imitators: Individuals who only buy if already others have bought the product since they are influenced
by word of mouth or so-called ‘internal communication’ (Mahajan, Muller et al. 1990)
Strictly speaking the Bass diffusion approach does not need to assume that people belong to the ”innovators” or
“imitators” categories, it is possible that each person’s behavior is partly dictated by innovation and partly by
imitation.
Following this narrative the number of first time purchases can be expressed as follows:
!! =!"!!" = ! ! − !! + ! 1!!! ! − !! ! (1)!
37
With:
⋅ ! n!:!product!purchases!in!period!t!⋅ N!:!cumulative!product!purchases!until!period!t!⋅ M:!(cumulative)!market!potential!in!product!life!cycle!⋅ p:!coefficient!of!innovation!!⋅ q:!coefficient!of!imitation.!
Alternatively the model is sometimes written as (this may turn out useful looking at some authors results who use this
notation):
! ! = (! + !" ! )(1 − ! ! )! (2)!
With
f(t) density function of sales at time t,
F(t), fraction of the potential that is achieved at time t.
Integrating over time, the total fraction of the potential that has adopted technology at time t is:
! ! = 1− !!! !!! !
1+ !! !! !!! !
!
(3)!
And the sales at period i are:
X(i) =m((F(ti)-F(ti-1))+ui (13)
4.2. Modeling diffusion in MMEM
MMEM uses the Bass model to estimate the diffusion of new propulsion technologies. Emerging technologies are
defined as those whose potential market shares in the first period are significantly below the observed market shares
as forecasted by MMEM. This is the case for all technologies regarded in MMEM, except diesel and gasoline vehicles.
The application of the Bass diffusion model within the MMEM follows a three-stage approach:
! Estimation of the average segment-weighted potential for technology in each point in time,
! Estimation of the achieved market share forecasts (that consider diffusion aspects) using the simple
Bass model for each technology in each time step,
38
! Redistribution of the unachieved market potential on established technologies.
The individual stages are described in detail below.
MMEM uses a discrete choice modeling approach to simulate purchase probabilities for different competing
technologies. Forecasts are produced separately for several segments of buyers – e.g. household purchases, corporate
purchases, Dienstwagen purchases, etc. However, as word-of-mouth effects are not restricted to one particular
segment of the car market, the average market potential of alternative technologies are needed for the entire
German car market. Therefore, in order to derive the average market potential, the buyer market segment-weighted
average of the potential of each technology is calculated. Currently, the MMEM has the average market potential for
each of the nine competing propulsion technologies at each point in time. This set of market shares forms the market
potential which enters the Bass model in step two.
The market potentials (described in stage 1) for each technology are regarded in Bass models.
Purchases for each time step are cumulated and enter the Bass model. That is all the data needed to produce
estimates of which share of the market potential will be achieved in each time period. The chart below exemplarily
illustrates how market potentials and achieved purchases are modeled for plug-in hybrid electric vehicles market
shares.
@,91;(&5A6&""!"&"0/(.&;(81.'8&:0;&2-;L('&J0'(+',-.&-+/&;(-.,Q(/&2-;L('&8*-;(8&0:&DM!R&
Source: MMEM
Finally, the unachieved market potential is re-allocated as otherwise the sum of the market shares of each technology
would not equal 100%. We argue that, as market potential is not yet achieved for emerging technologies (due to
inexperience with the technology), prospective buyers turn to buying established technologies instead. That is, the
difference between market potential – as produced by the MMEM – and the market share suggested in the Bass model
will be allocated to existing technologies. The allocation of the unachieved potential is done according to the share of
an individual established technology among all established technologies.
39
4.3. Values for p and q
Expert view
Expert views suggest values of p around 0.03 and q around 0.4 as valid assumptions across a number of different
technologies. (personal communication with Prof. Eric Kroes, Vrije Universiteit Amsterdam, January 2011)
These indications can be confronted with estimates suggested in the literature:
Cao, 2004
Cao (2004) provides estimates for sales of alternative fuel vehicles on the Californian market.
Starting from equations as below (in continuous and discrete time)
f(t) is the density function of purchase at time t;
F(t) is the cumulative fraction of adopters at time t;
m is the number of ultimate adopters, or market potential;
S(t) is the incremental sales or number adopting during time period t;
N(t) is the cumulative sales or cumulative number of adopters through time period t;
p is the coefficient of innovation; and
q is the coefficient of imitation.
Cao made two general assumptions quoted here to give the full picture
(1) “We relaxed one major assumption underlying the basic first-purchase Bass model — we assumed that the market
potentials of AFVs do not keep constant but vary as explanatory variables change.” (Cao 2004, p. 58).
(2) “We assumed that the market potential of HEVs is around 10% of total car and truck registrations in 2000.” (Cao
2004, p. 68).
In subsequent years, the potential is supposed to vary as a function of awareness about the existence of HEV and
(lagged) fuel price such as in the equation depicted below. As noted by Cao, the estimated innovation coefficient
varies strongly when the initial market diffusion assumption varies.
! ! !! ! ! ! ! !!! !
!! !! !! !!! !!
"<$!
40
F(t)=1(e-〖 〖_^(-(p+q)t)〗〗^ )/(1+p/q e^(-(p+q)t) ) (14)
Cao uses the additional feature that m is a function of awareness and fuel price (with his notations):
! ! = 0.045. !.!" ! !! ! − 1 . ! ! − ! ! − 1 + ! ! ! (5)!
With :
a : impact of awareness on potential.
S(t), sales at time t
Pa, fuel price,
Other variables as previously defined.
The estimates are shown in Table 14 below.
Technologies Innovation coefficient
P
Imitation coefficient
Q
Predicted total market potential m
Bioethanol (p.66) 0.00441 0.491 245971
CNG (p.58) 0.021 0.265 100371
Hybrid*,** (p.70) 0.000446 0.4788 Exogenous assumption 10% of total registered cars in 2000
Table&14:&Bass&coefficient&values&estimated&by&(Cao&2004)&
*refers to model 2 which Cao has chosen as his “final best model”. The 10 % assumption is exogenous. It derives from
the EIA scenario of 19 mio HEV sales 2001-2025, compared with the 220 mi. vehicles registered in 2002, which roughly
equate total sales in the period 2001-2015 to 10 % of the current registered fleet (see p.68).
This model the market potential is supposed to vary at each time step, starting from a 10 % of total registrations, as a
function of “awareness” and lagged fuel price.
These values are fairly untypical. We observe that the coefficient values estimated by Cao imply a very weak
innovation adoption and a strong imitation effect. This will typically result in a diffusion curve that will have a lengthy
start-up phase and a steep increase of diffusion in a second phase.
It can be assumed that the very low p values result from the fact that the empirical data series available at that time
were only incipient. The author (Cao 2004, p. 68) reports: “Honda Insight, the first HEV [‘hybrid’ in MMEM terms]
model in the U.S., was introduced in December 1999. Thus the number of observations on HEV sales is even smaller
than that for CNG and E85 [‘bioethanol’ in MMEM terms] vehicle sales. Currently, our HEV data contain the annual
sales for only four consecutive years: 2000-2003.” In particular, one could doubt that the diffusion of Hybrid had
reached its inflexion point at that time, casting doubt on the whole estimation.
Lamberson
Lamberson 2008 estimates parameter values of a Bass model and a Gompertz model by the method of non-linear least
squares to forecast the diffusion of hybrid electric vehicles in the USA (monthly vehicle registration data from
February, 2001 to October, 2007) , see Lamberson 2008, p. 10.
Table 15 provides the results (with conversion to annual value for comparison):
Bass Innovation Imitation coefficient q Predicted
41
coefficient p total market potential m
0.0000515 (monthly) 0.000618 (approx. annual)
0.0728 (monthly) 0.8736 (approx. a.)
1600000 (monthly) 19200000 (approx. a.)
Gompertz Coefficient a Coefficient b Predicted total market m
11.2 (monthly) 0.0118 (monthly) 25700000 (monthly)
Table&15:&Coefficient&values&for&a&Bass&diffusion&model&and&a&Gompertz&diffusion&model&&
Source: Lamberson 2008
Steffens, 2003
Steffens 2003 suggests an extended Bass model by taking multiple unit ownership of color televisions and automobiles
into consideration. The reported p and q values for the first car purchase models, referring to the Australian market
1966-1996, are p=0.0076 and q=0.090 Steffens 2003, with θ (proportion of the population that can adopt the
innovation) equal to 0.914.
The limitation of these estimates, for our purpose, is that they relate to mature technologies and to market
conditions (time framework, location) that do not easily transfer to the German electric mobility context.
Becker, 2009
Becker, Siduh et al. 2009 apply values of p= 0.01, 0.02 or 0.025 for three different scenarios, a value for q=0.4 for all
three scenarios. m is estimated as a percentage of 70% or 90% of the light-vehicle market in each year. However, the
justification for these estimates seems at least very weak; they just rely on the interval of values found by Mahajan et
al. 2004 on a series of technologies and probably are not a valid source of data – (Becker, Siduh et al. 2009, p.31).
Gross, 2008
Gross 2008 assumes Bass parameter values p=0.01 and q=0.1. Based on direct communication with the author, he
states that he relies on suggestions by Bass 2004 und Schneider 2002 to select these values.
4.4. Estimation based on market data
In order to complement studies about p-and q-estimates, a proper estimation could be made based on German market
data.
Available data
The estimation requires time series of sales of the relevant technologies. As far as the German car market is
concerned, the Kraftfahrtbundesamt (KBA) provides monthly new registrations data – in MMEM terms, new purchases
n_t - with decomposition by technology.
Monthly data:
42
! New registrations for gasoline, diesel, liquefied petroleum gas (lpg), compressed natural gas (cng) and
hybrid are available from Jan. 2007 to Jan. 2011.
! New registrations for electric vehicles (BEV in MMEM terms) and “other” technologies (which are not
described from KBA) are available on a slightly more restricted time frame from Jan. 2007 to Dec. 2008.
Yearly data:
! New registrations for the aforementioned technologies are available for the period 2005 – 2008.
Additionally, new registrations data aggregated across all technologies are available.
The relatively limited quantity of data available challenges the feasibility of an estimate. On this issue we report the
synthetic assessment made by Cao.
In the Bass model, the estimation of three parameters (p, q, and m) is required to understand the diffusion of an innovation. Accordingly, the sales data for at least three points in time are necessary to accomplish the estimation process. However, the estimates of these parameters are sensitive to the number of observations available to the estimation (Mahajan, et al., 1990a). Heeler and Hustad (1980) suggested that stable and robust parameter estimates can be obtained only if the data under consideration contain at least ten observations and include the peak of the non-cumulative adoption curve, which is supported by the results of Srinivasan and Mason (1986). However, waiting for enough observations to develop reliable models is “too late to use the estimates for forecasting purposes” (Mahajan, et al., 1990a, p.9), and therefore makes the prediction useless (Hyman, 1988). Caught by this fundamental paradox, most operationally useful (as opposed to post-hoc diagnostic) diffusion models are calibrated on relatively little data and hence are not necessarily stable. However, this is a virtually inescapable feature of this modeling approach.
For estimation of the Bass parameters, the cumulative new vehicles’ registrations N_t of a given technology are
necessary. For hybrid, we estimate the initial (2008) stock of Hybrids based on annual data, 2005-2008. In doing this,
we check for years 2007 and 2008 whether yearly data and monthly data are compatible. The correspondence is
nearly perfect for 2008 and exhibits marginal deviations for 2007. The evolution of cumulative vehicle registrations is
provided in the following figure:
@,91;(&5F6&T121.-',<(&<(*,).(&+(H&;(9,8';-',0+8&:0;&/,::(;(+'&'()*+0.09,(8&SW()X&=OOK&Y&Z-+X&=O55U&
Source: MMEM
43
For Bass parameter estimation purposes these time series of cumulative new registrations N_t are matched with the
new registrations n_t in the same time frame.
Based on these data, MMEM estimates Bass parameter values using two different ways, depending on whether the
parameter m (the market potential in product life cycle) is endogenous in the estimation or whether it is determined
exogenously:
1 – estimation of p, q and m based on the sales of the technology in the first years of diffusion.
2 – estimation of p and q based on the sales and an exogenous potential m.
4.5. Estimation with model-endogenous market potential
First, the coefficients p and q are estimated together with the market potential m of a technology. The dependent
variable is new registrations n_t, the explanatory variable is cumulative purchases N_t until period t.
Starting with the standard equation of the Bass diffusion model, transformation for estimation purposes results in:
!! =!"!!" = !" − !!! + !!! −
!!!!!! (6)!
and in a second step:
!! =!"!!" = !" + (! − !)!! −
!!!!!! (7)!
Or
!! =!"!!" = !! − !!!! + !!!!!! (8)!
with!b! = pM,!!b! = q!p,!and!b! = ! !!.!
Once b0 to b2 are estimated, the values p, q and m are retrieved if a solution exists. The results are reported in the
following table. For electric vehicles two negative values are estimated, so the absolute lower value is reported. For
hybrid and other fuels there exists no (real) solution for the p and q, because the square root of Δ is negative.
b!b! = !qp and division by q yields: !!!!! = !p! (1) Insert (1) in – b! = q!p, yields – b! = q + !!!!! , and addition of b!
yields: q + b! + !!!!! = 0. Multiplication by q yields: q! + b!q + b!b! = 0.
This second order polynomial has the following roots : q!/! = ! !!! ±!!!!!(b!b!)
The index on q indicates that there are possibly up to two solutions. Mostly we receive one positive and one negative
value, but only positive values are acceptable. In this case the only positive value is reported. If both solutions are
negative, the lower absolute value is reported. A solution exists if the square root term is not negative. If the square
root term is negative, p, q and m values cannot be calculated. In this case the term “no result” is reported.
The parameter values are estimated by the nonlinear least squares (NLS) approach. NLS are usually preferred to OLS.
Reasons for this are that correlation between the different explanatory variable may imply large errors in the
estimates of each individual coefficient. Additionally OLS does not provide distribution for p and q (but only for a, b,c)
making it impossible, or at least unpractical to make hypothesis testing about the real value of the estimated
parameters. Additionally, as indicated by Cao, “the attempt to estimate a continuous model with discrete time-series
data may lead to time-interval bias, which tends to overestimate adoption if cumulative adoption grows sharply, and
vice versa (Schmittlein and Mahajan, 1982). Empirically, using smaller time intervals, say quarterly data instead of
annual data, will reduce temporal aggregation bias (Putsis, 1996).”
44
Alternatively, MLE and NLS techniques were proposed to estimate the parameters directly from the differential
equation of the diffusion model (Schmittlein and Mahajan, 1982; Srinivasan and Mason, 1986). According to Srinivasan
and Mason (1986), the model formulation of the NLS technique is as follows:
S(t) = m[F(t) − F(t −1)] +ε (t) . (3)
The model formulation for MLE is specified in Schmittlein and Mahajan (1986). Both techniques provide standard
errors of the parameters and eliminate the time-interval bias, superior to the OLS procedure. However, they have
their own limitations. For both approaches, an initial value for each parameter is required to estimate the Bass model
and the parameter estimates are sensitive to the initial values. The OLS estimates could be used to provide the initial
values for these parameters (Srinivasan and Mason, 1986). Specifying different initial values is also highly
recommended in practice (Putsis and Srinivasan, 2000). Further, model estimation may not converge in some cases,
partly due to poor initial values (Judge, et al., 1985).
The NLS approach is generally better than MLE since in their MLE formulation Schmittlein and Mahajan “consider only
sampling errors and ignore all other errors, such as the effects of excluded variables and the misspecification of the
probability density function for adoption time”, and thus are likely to underestimate the standard errors of these
parameters (Srinivasan and Mason, 1986, p. 178). Mahajan, et al. (1986) compared these estimation techniques
empirically and found an overall superiority of the NLS procedure. A simulation study by Srinivasan and Mason (1986)
demonstrated that the biases in the parameter estimates are small, less than 7%. Recently, Van den Bulte and Lilein
(1997) stated that the estimators in the NLS estimation procedure are consistent but not unbiased. Specifically, by
examining both empirical and simulation data, they found that NLS tends to underestimate m and p and overestimate
q to a much greater extent than Srinivasan and Mason (1986) did. They pointed out that the amount of bias depends
on the amount of noise in the data, the number of observations, and the differences between the cumulative
penetration of the last observation and the true saturation penetration. Thus, one possible solution to minimize the
bias is to increase the number of data points, which is consistent with the findings of Putsis (1996).
Bass' parameters b0 b1 b2 p q m
liquefied petroleum gas (incl. bivalent) 248.805 0.096 0.000 0.006 0.102 42268.897
compressed natural gas (incl. bivalent) 534.952 0.068 0.000 0.013 0.081 40836.964
Electric -0.736 0.374 -0.006
-0.012 0.362 60.748
Hybrid 516.430 0.002 0.000 no result
no result no result
other fuels 27.508 -0.570 0.003 no
result no
result no result
Table&17:&Bass¶meter&estimation&issues&
Source: MMEM (2011)
45
4.6. Conclusion on Bass parameters
From our time series of cumulative new registrations we know that there were already about 44.400 LPG and 57.800
CNG vehicles in the German car market in 2010. So the model-endogenously estimated market potentials reported in
Table 17 are unrealistic low. For the other technologies partly negative coefficient values are estimated or no results
are gained. All in all the gained results are unsatisfactory.
Estimation with model-exogenous market potential
In this section, MMEM considers a situation in which m is defined a priori independently of the estimate of p and q.
The reasons for doing so are that model-endogenously estimated market potentials are not realistic. This echoes
certain findings in the literature. Van den Bulte and Lilien N. N., p. 2045 claim that in studies “the estimated
population size [meaning market potential m] is close to the cumulative number of adopters observed in the last time
period for which data are available.” They claim that this approach results in an “upward bias in the contagion
[imitation] parameter.” So MMEM adopts a pragmatic solution to this issue determining the market potential m Bass-
model exogenously. However, when calibrating p and q based on exogeneous potential assumptions, much attention
should be dedicated to the quantification of m. This quantification is however not an easy. This is illustrated by the
general lack of solid argumentation on m estimates that we found in the existing literature.
The estimation of Bass parameter values proceeds as follows, starting again from section 4.1.
This equation can be rewritten as:
!! !!"!!" ! !!! ! !!!! "C$!
with !! ! !!!! and !! ! !!!!!!!!!!.
If m is given, both !! and !! are known. This makes it possible to estimate p and q.
Finally, we investigate the sensitivity of the Bass p- and q-parameter value estimates to the market potential figures.
We consider market potentials m = 0.1, 0.5, 0.7, 1 , 5 , 10 and 20 million vehicles to obtain the following Bass p- and
q-parameter values from monthly data of new registrations for December 2008 – January 2011 and cumulative new
registrations since 2005:
3-4.(&5K6&!8',2-'(/&Bass p-parameter values for different model-exogenously determined market potentials
Source: MMEM
3-4.(&5N6&!8',2-'(/&$-88&[IJ-;-2('(;&<-.1(8&:0;&/,::(;(+'&20/(.I(709(+018.?&/('(;2,+(/&2-;L('&J0'(+',-.8&
Source: MMEM
46
The results of the estimate are, of course, dependent on the assumed market potential for hybrid cars in Germany.
We argue that a market potential of 10 million cars is a reasonable estimate for the size of the potential market (m).
The estimate is derived from a utility function that is used to produce market shares for competing propulsion
technologies. Our model suggests an average market share of hybrid cars over the next ten years of 25% before
diffusion is accounted for. Applying the 25% to the stock of cars in Germany of around 40 million suggest a market
potential of 10 million cars.
For comparison with results for Bass p- and q- parameter values from other studies from these p- and q-values for
monthly time series and an assumed market potential of 10 million hybrid vehicles also annual p- and q-values are
approximately estimated by multiplication with the factor 12. So for hybrids a range for annual p-values of 0.0000156
to 0.0000516 and an annual q-value of 0.288 are estimated. For comparison reasons also p- and q-values from time
series with annual periodicity are calculate.
Additionally p- and q-models for annual data from KBA and the period 2005 till 2010 are estimated. We gain:
Market potential in mil l ion vehicles
0 .1 0 .5 0 .7 1 5 10 20
p 0 .025291 0 .006884 0 .004974 0 .003510 0 .000712 0 .000356 0 .000178
q 0 .336419 0 .188109 0 .180887 0 .175681 0 .166418 0 .165300 0 .164744 3-4.(&=O6&!8',2-'(/&JI&-+/&[&J-;-2('(;&:0;&/,::(;(+'&2-;L('&J0'(+',-.8&Y&-++1-.&',2(&8(;,(8&Y&M?4;,/&
Source: MMEM
Also, in variants of these models, GDP growth rates and a dummy variable for car scrappage scheme (so-called
“Abwrackprämie”) are introduced as explanatory variables in the models. Since it is found that these variables are
insignificant or have no additional explanatory power they are discarded.
Toyota Deutschland GmbH 2011 reports annual sales volumes for Toyota Hybrid vehicles (incl. Lexus) in Japan,
overseas and overall (which is the sum of both) for the years 1997 until 2010.
@,91;(&=56&)121.-',<(&30?0'-&8-.(8&<0.12(8&5NNG&I&=O5O&
Source: MMEM, adopted from Toyota figures
47
From these time series Bass p, q and m parameter values are estimated:
@,91;(&==6&!8',2-'(/&;(9;(88,0+&)0(::,),(+'8&-+/&$-88\&J%&[&-+/&2&)0(::,),(+'8&:0;&30?0'-&M?4;,/8&
Source: MMEM
Discussion of the results
We find that, based on the data available, p and q values are uncharacteristically low compared with estimates found
in the literature review.
We find that the estimates for p and q suggest an untypically sluggish uptake. In fact, the results are significantly
lower than comparable empirical results from the literature review. Compared to, for example, the parameters
provided by Lamberson (which are already characterized by a very low p value), the benchmark-time (2020) value is
shifted by 15 years into the future. Because of the very flat dynamic of the dataset, the nature of the Bass-equation
and the very high (compared to the number of cumulated sales in the dataset) market potential, it is no surprise to
find the market penetration process estimated from the dataset being very slow. Since our dataset only reflects the
very beginning of the diffusion process we can, for an estimate, expand the Bass-equation around zero (analyze the
function in a neighborhood of zero, discard higher orders of the respective variable) and find a linear dependence of p
on the market potential.
Indeed, it becomes apparent that the market potential is the main driver of the diffusion speed. Lower market
potentials lead to a linear increase in the estimate of p and vice versa, while q proves to be rather stable.
The two available data sources that most closely match the German electric mobility market (Cao and Lamberson)
exhibit anomalous low values for the p coefficient.
Authors Innovation coefficient
P
Imitation coefficient
q
Literature estimates Cao, 2004 (Hybrids) 0.000446 0.4788
(Lamberson 2008) 0.000618 0.8736
Steffens, 2003 0.0076 0.0905
Becker, 2009 0.01, 0.02 or 0.025 0.4
Gross, 2008 0.01 0.1
MMEM estimates MMEM (KBA monthly time series, 47annualized parameter values)
0.0000156 - 0.0000516
0.288
MMEM (KBA annual time series) 0.0001 – 0.0007 0.165
MMEM (Toyota time series, worldwide-sells)
0.00125 0.35579
MMEM conclusion 0.001 0.3
3-4.(&=A6&$-88&J-;-2('(;&""!"&)0+).18,0+&
Source: MMEM (2011)
48
49
5. Car industry and CO2 optimization in MMEM
In 2009 the EU issued regulation 443/2009 regarding CO2 emissions of passenger cars. As this has an important impact
on European car markets we give a detailed explanation regarding the regulatory outline. Car makers will be forced to
respect this regulation with new car designs and technological improvement. We try to model this reaction as an
optimization process which aims at minimizing the cost of introducing technological change to meet the regulation.
We shall give an overview of the underlying methodology of optimization and the car makers’ options and behavioral
patterns stemming from this.
In 2009 the EU issued regulation 443/2009 regarding CO2 emissions of passenger cars. As this has an important impact
on European car markets we give a detailed explanation regarding the regulatory outline. Car makers will be forced to
respect this regulation with new car designs and technological improvement. We model this reaction as an
optimization process which aims at minimizing the cost of introducing technological change to meet the regulation.
Also endogenous technological improvement in the passenger car market is reflected in this approach as optimization
measures will have a direct impact on cars’ performance. We shall give an overview of the underlying methodology of
optimization, car makers’ options and responses stemming from this.
EU-Regulation 443/2009
The ultimate goal of this 2009 issued regulation is to control and reduce European average fleet CO2 emissions, which
is pursued by setting common emission targets. The latter are introduced on a per manufacturer basis and amount to
130g/km starting from 2012 and 95g/km starting from 2020.
Fleet averages are calculated by considering the specific emissions of all newly sold vehicles, within a relevant one
year time interval. Specific emissions are measured by testing the vehicle’s emissions during the NEDC and an adapted
version of the NEDC for full electric drive hybrids.
However, these are only basic values which are to be adjusted to the manufacturer’s specific situation. The first
adjustment is of a transitory nature, allowing the manufacturers to neglect a fixed percentage of cars building their
fleet, rendering them able to discard the most polluting vehicles and thus effectively reduce their average. Starting
from 65% in 2012, increasing stepwise (see Figure 24), manufacturers will have to consider their actual fleet only in
2015. The second adjustment is a weight scaling applied to the manufacturer’s specific emissions target (units
suppressed):
Specific!emissions!target = General!emissions!target+ 0.0457*(M!M!), where M is the mass of the vehicle and M0 the manufacturer’s fleet’s average weight. The average weight is taken
over a 3 years’ time period, starting from 2016. Before 2016 M0 is constantly set to 1372kg.
The third adjustment directly affects average fleet emissions and is called super credits. Super credits are awarded to
manufacturers of so called zero emission vehicles (emitting below 50g/km of CO2), making those cars count as more
than a single vehicle towards average fleet emissions (e.g. a zero emission vehicle does not count as one but as 3.5
vehicles in 2012). The time dependent super credit multipliers are shown in Figure 24.
Exceeding fleet emission targets is penalized with an increasing premium (5€, 15€, 25€, 95€ per g/km per car sold for
1g/km, 2g/km, 3g/km or more than 3g/km exceeding the target) during the years 2012-2018 and a fixed premium of
95€ per g/km per car sold from 2019 on.
In the MMEM EU-Regulation 443/2009 has been implemented under the assumption that car manufacturers will pass on
premium costs due to excess emissions to the customers. Premiums are distributed analogous to the CO2-emissions
resulting in higher shares of premiums absorbed by cars featuring high emissions. Note that premiums are not only
passed on but are also subject to taxes when added to the purchase price.
50
The introductory part of the regulation, described in the third paragraph of this chapter has been implemented as an
effective target rather than a cut-off to the fleet.
Technical development of cars
Considering that most German car manufacturers score an unadjusted average of around 150g/km the impact of this
regulation on manufacturers will be substantial. The need for emission reduction and thus technological improvement
arises.
Manufacturer costs of emission reduction
Potential for emission reduction can be found in many aspects of today’s cars. For example downsizing and weight
reduction, reduced friction losses, advanced cooling or piloted gearboxes, assisted steering, etc. Potential emission
reduction measures have been collected and evaluated by the TNO (TNO 2006). Their results in the shape of cost
curves for emission reduction have been incorporated into the MMEM.
The original cost curves in the TNO report contain hybridization as means of emission reduction. Since the MMEM
explicitly distinguishes hybrid technologies it was necessary to recalculate the cost for emission reduction excluding
any hybridization measures. This has been done by replicating the TNO approach of listing all possible combinations of
emission reduction measures and fitting a third order polynomial to the resulting data set. Also the maximum emission
reduction potential has been recalculated for the MMEM situation. The resulting cost curves are shown in Figure 27-32,
the parameters in Table 25. Note that the corresponding, general function:
! = ! ∗ !! + ! ∗ !! + ! ∗ !
where y is improvement costs in euro and x is emission reduction in grams per kilometer, is only valid in the interval
ranging from zero improvements to the maximum potential emission reduction also given in Table 25. The TNO report
originally featured cost functions for Diesel and Gasoline cars in three different sizes. This poses problems concerning
a direct implementation into MMEM as its structure distinguishes between more segments and different technologies
and thus requires a finer cost structure for more technologies.
The issue of finer segmentation was addressed by expanding the TNO segments (small, medium, large), as shown in
Error! Reference source not found.. This has been done considering that the initial MMEM tailpipe emissions must
not exceed the corresponding TNO reference car's emissions while matching emission values as closely as possible.
For mild hybrids the cost curves have been newly estimated, (taking the MMEM average emissions of the projected
vehicle segments as reference vehicles). The corresponding parameters are also shown in Table 25. The cost curve can
be seen in Figure 30 - Figure 32. Plug-in-Hybrids and Range-Extenders are not treated by the optimization feature. As
a simplification we assume their conventional engines to stay at the current level of efficiency for they only provide a
small share of the cars kinetic energy. Expectations here are that all technological advancement is focused around the
electric part of the drive.
Following the TNO assumptions, manufacturer costs are taken into account as running costs entirely, fixed costs (e.g.
development costs, production rearrangements etc.) are not considered explicitly but as a proportional share (see
cost definition given on p. 29 in (TNO 2006)). All improvement costs are additionally considered for reseller margins
and VAT. Optimization costs after margins and VAT are directly additive to purchase prices.
Customer benefits of emission reduction
Directly connected to emission reduction is always a reduction in fuel consumption. Especially for conventional cars
those effects are proportional. So emission reduction will ultimately lead to less fuel consumed which is, of course,
beneficial to the customer in several ways. Most importantly there is a direct monetary advantage and from a highly
future oriented point of view less fuel consumed also somewhat takes away the anxiety of ever rising oil prices.
51
Additionally less refueling means time saved. Higher fuel efficiency also contributes to the “green”-feel which has
risen in importance over the last decades with the ongoing debate of climate change and which might become an
indicator for social status.
In this regard the MMEM only depicts the most direct effect of reduced consumption. Recent studies (Öko-Institut and
ISOE 2011) have shown that as of now CO2-emissions play a negligible role in determining which car to buy and oil
prices are deterministically introduced in the model so that it would be artificial and inconsistent to add uncertainty
over it somewhere else in the model.
The MMEM consumer values each Euro saved over 100 km driven by less fuel consumed with an average of 576€ in
his/her purchase decision. Furthermore it is assumed that the car industry is well aware of this willingness to pay
towards fuel cost reduction and estimates the optimized car's additional attractiveness on the market accordingly.
The monetary advantage of reduced fuel costs then calculates as follows:
"adv." "Fuel Cost"=WTP("Fuel Cost" )*"Init.Fuel Cons."*"Emission Reduction(%)"
"Emission Reduction" ("%" )=("Init.Emissions -Emission Reduction)/Init.Emissions"
This approach is a simplification of a car maker run estimation of market development under the effects of
optimization measures. It covers the initial impact on the utility function, and thus on the purchase decision but it
does not cover the full impact on the market.
Manufacturers’ maxim of acting in MMEM
Car manufacturers take into consideration the sum of extra costs conveyed to the customer. This includes, on one
hand, the fines due to excess emissions and optimization cost as described above and, on the other hand, fuel cost
savings due to optimization measures. In MMEM car manufacturers try to minimize the total costs conveyed to keep
their products as attractive as possible under regulation 443.
It is assumed that decisions across segments are taken individually, adjusting each segment independently in terms of
technical measures taken. Car manufacturers will tend to those segments first which promise the highest beneficial
impact, iteratively repeating the process of consideration followed by optimization measures until there is no cost
reduction potential to be detected at any given time step of the simulation.
There is no delay between car manufacturers’ decisions and implementation of those changes in their cars. This is a
simplifying approach to long term thinking under well-known or predictable conditions, for the corresponding real
world decision or respectively its associated actions are not executed ad hoc. Thus the model depicts car
manufacturers with perfect knowledge about future developments in regard to market conditions as well as
requirements in terms of emissions.
While this approach is not a total profits but rather a cost consideration it still approximates well rational market
behavior for the sole reason of the market structure in MMEM being a monopolistic competition. An optimal long term
strategy in such markets generally is cost efficiency as in equilibrium there is zero economic profit.
Car manufacturers in MMEM always optimize to a point where it is either not possible to improve cars anymore or
where described cost balance dictates an optimal level of optimization. This reflects the fact that MMEM takes into
consideration running costs connected to optimization measures only. In that sense a viable long term strategy (after
2020) also includes undoing optimization measures if regulation fines and user benefits allow for it. We restrict that
type of strategy to be taken into account only after 2020 because the regulatory targets are well known which leads
to the conclusion that they found their way into car manufacturers’ strategies already and that preliminary actions
have been taken accordingly. While this purely rational approach to acting maxims seems to conflict with car
manufacturers' stated preferences of fulfilling any type of regulation it does not conflict with the fact that the
52
German automotive industry pays millions of fines every year for introducing high emission vehicles to the US car
market (NHTSA 2011).
Expected impact of optimization measures on the market
Optimization measures will noticeably impact market development via two opposing effects as they do not only evoke
an increase in purchase prices but also alter cars’ economic performance positively.
Forcing car manufacturers to increase fuel efficiency by any type of emission regulation leads to a general decrease in
attractiveness of modified cars. This is a simple result of the balance considered with optimization decisions. In
equilibrium marginal increases in fuel efficiency result in increases in purchase price which cancel each other out
concerning customer benefits. When a regulation (i.e. EU443/2009) is introduced the point of equilibrium is shifted
towards higher fuel efficiency as potential fines soak up parts of additional costs. Another way to access this decrease
in attractiveness is to take a look at total costs. In absence of a regulation total costs are minimal in equilibrium. The
introduction of additional costs in the form of fines increases the total sum. These costs have to be covered by
somebody, which, in this case, is the customer as the manufacturer just passes them on. This leads to a decrease in
attractiveness.
@,91;(&=B6&]1.(&BBA&J-;-2('(;8&,+&""!"&
Source: MMEM (2011)
53
Vehicle type a (€*(g/km)- 3)
b (€*(g/km)- 2)
c (€*(g/km)- 1)
Maximum emission reduction (g/km)
Gasoline - small 0.003870 -0.1880 38.60 76.6
Gasoline – medium 0.002165 -0.1398 34.89 97.0
Gasoline – large 0.001226 -0.1098 32.86 125.4
Diesel – small -0.013066 1.0920 31.05 41.3
Diesel – medium -0.005848 0.6803 30.57 52.2
Diesel – large -0.001388 0.3480 29.97 68.6
Hybrid – small 0.004801 -0.2373 43.36 68.2
Hybrid - medium 0.003071 -0.1765 39.20 86.4
Hybrid – large 0.001738 -0.1386 36.92 111.6 Table&25:&TNO¶meters&for&fuel&efficiency&improvement&
Source: MMEM (2011)
MMEM-Segment TNO-Segment
Mini Small
Kleinwagen Small
Kompaktklasse Medium
Mittelklasse Medium
Obere Mittelklasse Medium
Oberklasse Large
Geländewagen Large
Sportwagen Large
Minivans Medium
Großraumvans Medium
Util i t ies Large Table&26:&equivalence&between&TNO&segments&for&fuel&efficiency&improvement&and&MMEM&segments&&
Source: MMEM (2011)
54
Figure 27: Adapted TNO-Curve Gasoline large
& &
@,91;(&=K6&P/-J'(/&3^#IT1;<(&V-80.,+(&2(/,12&
Figure 29: Adapted TNO-Curve Gasoline small
&
&
55
@,91;(&AO6&P/-J'(/&3^#IT1;<(&W,(8(.&.-;9(&
@,91;(&A56&P/-J'(/&3^#IT1;<(&W,(8(.&2(/,12&
@,91;(&A=6&P/-J'(/&3^#IT1;<(&W,(8(.&82-..&
56
@,91;(&AA6&P/-J'(/&3^#IT1;<(&M?4;,/&.-;9(&
@,91;(&AB6&P/-J'(/&3^#IT1;<(&M?4;,/&2(/,12&
Figure 35: Adapted TNO-Curve Hybrid small
57
6. Energy sector
6.1. Introduction
The transport sector in Germany accounts for about one quarter of domestic energy consumption. Oil , with more than 90 percent, is the largest primary resource on the supply side. By contrast , electricity contributes by less than 3 percent to the energy supply of the transportation sector. Electromobil ity wil l shift this relat ionship over the long term, when a growing number of car owners wil l rely on electricity as the primary energy source. Such a reorganization of the structure of demand wil l reduce dependence on imported fossi l fuels, because the additional demand may be met with domestic energy sources if an increasing share of local ly avai lable renewable energies l ike wind and solar are deployed. From the perspective of the mitigation of cl imate change, the higher efficiency of electric motors compared to conventional internal combustion engines wil l decrease harmful emissions, but it wil l not necessari ly lead to zero-emission cars: Depending on which power plants are deployed to meet the additional demand, greenhouse gases may result from the combustion of domestic l ignite, coal or natural gas power plants. The interaction between electric mobil ity and the energy sector is not l imited to the supply of electricity and the related emissions, but also encompasses the infrastructure that is used to charge the electric cars, including
! individual!charging!devices!! a!network!with!fastEcharging!stations!for!pure!batteryEelectric!vehicles!!! a!network!infrastructure,!including!access!to!information!and!communication!interfaces!
Electric cars can even actively participate in the electricity market. The increasing amount of t ime-varying electricity from renewable energy sources, especial ly wind and sun, leads to a greater need for the temporary storage of generated energy levels and associated network services than before. In comparison with other storage technologies, such as pumped storage power plants, compressed air storage or the gas grid, batteries of electric cars only have a l imited potential , though. Once car owners connect their vehicles to the grid, they can provide so-cal led anci l lary services to maintain the rel iabil i ty of the power supply by participating in the secondary market of minute reserves. This al lows electric cars to posit ively contribute to the stabil ization of the network and faci l i tates the transit ion to a low-emission power supply in a market with a high share of renewable energy. While the use of electric cars in the network has to be organized and coordinated, the individual car owners have to get appropriate incentives to connect their cars where ever possible with the network in order to obtain maximum benefits from their services. The technological context in which electric vehicles are deployed is characterized by an increasing convergence of the electricity sector with internet and communication technologies (ICT). The required coordination of the charging behaviour of the market and ramp-up of electric mobil ity coincides with the plan to create an intel l igent distr ibution network or "Smart Grid". Synergies may arise from the combination of different technologies with the help of appropriate policy instruments. The energy sector has tradit ionally been closely l inked to polit ical objectives and regulation. As one of the most important national infrastructure services, i t
58
contributes significantly to the qual ity of doing business in Germany. The sector-specif ic objectives are typical ly portrayed in the policy tr iangle of sustainabil i ty, energy security and efficiency. These policies often fol low not necessari ly complementary, but competing goals. While a carbon neutral energy supply should be achieved by developing renewable energy sources, the selective promotion of individual generation technologies leads to a distortion of the cost structure and thus contradicts the model of a competit ive electricity market. Moreover, the priorit ies change over t ime due to alternating polit ical necessit ies. Electric mobil i ty can posit ively contribute to al l three components of the energy policy tr iangle: sustainabil i ty and supply security are supported by shift ing from oil as a primary energy source for transportation services, as well as the contribution of cars to stabil ize the network, while efficiency can more easi ly be achieved by the innovative potential of electric vehicle technologies, and the emergence of new agents in the electricity market. The Market Model Electric Mobil ity al lows both a quantitat ive evaluation of the effects of short-and medium-scale policy measures as well as the representation of long-term developments in the interaction between electric cars and the energy sector. In addit ion to energy-economic indicators as a result of the model, the cost-benefit analysis provides a tool that predicts the volume of investments and the impact of the load on network behaviour and emissions by the year 2050.
6.2. Electricity prices
The main fuel input of electric vehicles is, by definition, electricity. Estimates of the electricity price vary slightly, but not as drastically as the oil price forecast, which will be discussed in the following section. Our simulation model uses the forecasts by Prognos-EWI-GWS (2010) until 2050, which is also the basis for the developments in demand and supply in the reference scenario. The price forecasts of this study show only minor changes over the simulation period and fluctuate in a narrow range of €0.21 to €0.22 € per kWh for households even in alternations of the Prognos-EWI-GWS scenarios.
The authors of the scenario explain the almost constant level of retail prices as follows: “On the one hand, rising CO2 and fuel costs increase prices in conventional generation. A growing share of renewable energies results in extra costs, too, despite learning curve effects. On the other hand, over time, the integration into a Single European market leads to an increased import of electricity, which is based on relatively cheap electricity from technologies abroad, and therefore a dampening effect on the electricity price.” (Prognos-EWI-GWS 2010, p. 117)
Prognos-EWI-GWS expects that effects are mutually cancelling each other out, but they may also shift into one or the other direction. In particular, an expansion of the European high-voltage grids and large-scale projects for the integration of renewable energies such as off-shore wind parks or photovoltaic applications could, under certain political conditions, increase the electricity price significantly. A group of German companies plans, for example, the project “Desertec” in the North African desert regions. Large-scale solar thermal power plants would feed into high-voltage direct current transmission cables (HVDC) power into the European grid. In two alternative scenarios, the Market Model Electric Mobility varies the forecasts of Prognos-EWI-GWS and assumes high and lower electricity prices, respectively.
59
6.3. Oil prices
The reference scenario of the market model is based on a number of assumptions that reflect the most probable trajectory of economic development of Germany and the international markets. One of the most significant factors that influence the success of electric vehicles is the price of oi l . If in emerging economies l ike China and India private, motorised transportation evolves in a similar form as in the industrial ized world, thereby increasing demand for oi l , coupled with stagnant oi l exploration levels, electric mobil i ty is l ikely to receive a double impulse: f irst , for countries without oi l reserves it wil l become increasingly attractive to decouple the energy demand of the transport sector from oil . For example, China could meet the electricity needs of its electric car f leet from electricity production based on its abundant coal reserves. On the other hand, the relatively high investment costs for the batteries of electric cars would be compensated by significantly lower variable costs for charging electricity and reduce the cost disadvantage (the so-cal led TCO gap) in the overal l cost of the vehicle significantly. In its forecast, the market model uses a conservative estimate of the increase in oi l prices over the simulation horizon. It refers to the prediction of the reference scenario of the Energy Information Administration of the U.S. Department of Energy, as published in the "International Energy Outlook" with annual oi l price forecasts unti l 2035. For the remaining t ime span unti l 2050, the oi l prices are l inearly extrapolated. In addition, the EIA provides a high and a low oil price scenario, which are used for the sensit ivity analysis . The following figure shows the historical data records on oil prices as well as the future trajectories.
Figure&36:&Oil&price&scenarios&of&the&EIA&
Source: EIA, Department of Energy (2011)
The calculations for the reference scenario yield an oil price of 143 U.S. $/bbl in 2050. As of the end of September 2011, the oi l price hovered around 86 US-$/bbl, which comes close to the predicted reference scenario.
6.4. Electricity supply
0!
50!
100!
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250!
1991!
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2003!
2007!
2011!
2015!
2019!
2023!
2027!
2031!
2035!
Const.&USI$/bb
l&
Historical!Values!
EIA!Reference!Scenario!
EIA!High!Price!Scenario!
EIA!Low!Price!Scenario!
60
6.4.1. Reference scenario
In the reference scenario, the simulation of the future electricity supply is modelled upon the forecasts of the study Energieszenarien für ein Energiekonzept der Bundesregierung“ (2010), which was conducted by the think tanks Prognos, EWI, and GWS. The study provides both instal led capacity ( in GW) and energy del ivered ( in TWh) unti l the year 2050. It takes into account the plans of the German government to significantly expand renewable energies, but assumes that fossi l fuels wil l not completely disappear in the domestic energy mix. The fol lowing figure shows the evolution of instal led capacity over the simulation period.
Figure&37:&Capacity&forecast&in&the&EWIIPrognos&reference&scenario&
Source: Prognos-EWI-GWS (2010)
One of the characterist ic features of the EWI-Prognos scenario is the significant r ise of instal led gas plants in the period between 2020 and 2030. In addit ion, coal and l ignite wil l st i l l be operated unti l 2050. The idea behind this capacity scenario is that despite al l efforts to switch to an entirely emission free electricity production, some reserve capacity wil l be maintained to compensate for t ime slots when no rel iable supply of renewables is avai lable.
6.4.2. SRU 509TWh scenario
To complement the EWI-Prognos scenario by a more ambitious scenario in terms of the deployment of renewable energies, an alternative prediction, issued by the German “Sachverständigenrat für Umweltfragen” (SRU) is used in the market model. This scenario also assumes a r ise in gas power plants, but predicts a stronger growth of offshore wind and solar capacit ies.
61
Figure&38:&Capacity&forecast&in&the&SRU&509&TWh&scenario&
Source: Sachverständigenrat für Umweltfragen (2010)
As the figure indicates, the decl ine of coal and l ignite plants is more drastic than in the reference scenario, and the German electricity is assumed to predominantly consist of wind, solar and gas in 2050.
6.4.3. MMEM 100% renewables scenario
To comply with rigid cl imate objectives, the market model features a third supply scenario. It has been exclusively developed by the MMEM team for the objective of this study and assumes that internationally produced renewable energies wil l be transported via DC transmission l ines to Germany, in particular solar energy from Northern Africa, and wind energy from the North Sea and the Balt ic Sea.
62
Figure&39:&Capacity&forecast&in&the&MMEM&100%&Renewables&scenario&
Source: MMEM (2011)
Due to the diverse geographic origins of the renewable energies, a high rel iabil i ty in supply can be guaranteed, such that the backup option of natural gas plants becomes obsolete.
6.4.4. Simulation of fluctuating primary energies
No power plant is avai lable 100 per cent of the year. Typical ly, power plants have to be temporari ly shut down for maintenance purposes for a couple of weeks or, in some cases, even months. For conventional power plants, including l ignite, coal , gas and nuclear, as well as for some renewable technologies (biomass, geothermal, hydropower and pump storage), the market model uses factors based on observations of German power plants, as reported by the association of German power uti l i t ies, BDEW. The following table provides an overview of the avai labil i ty per year (maximum 8760 hours).
Conventional Technology
Availability (Hours per year) Renewable Technology Availability (Hours per year)
Nuclear 8147 hr PumpStorage 8672 hr Coal 7534 hr Hydropower 3504 hr Lignite 8059 hr Biomass 7709 hr NaturalGas 5606 hr Photovoltaics 8760 hr Other Fossi l
5606 hr Geothermal 7884 hr
Table&40:&Annual&availability&of&conventional&and&renewable&generation&technologies&
Source: BDEW (2009)
In MMEM, we assume that the diverse range of power plants with these technologies leads to a stochastic shutdown of individual power plants for maintenance purposes
63
over the year, hence the avai lable capacity corresponds to the fraction of avai lable hours to total hours per year t imes the total capacity. Two renewable technologies have to be treated differently, though: Both wind and solar power exhibit a high degree of volati l i ty in their energy generation. While solar radiation, for obvious reasons, is l imited to day-time, wind has a profi le with different patterns according to seasons . For the simulation, the fluctuations of both wind and solar energy feed-in have been analysed. The original data stems from the four grid operators and has been aggregated. Observations are avai lable for the year 2010. Apart from daily f luctuation, wind data feed-in shows a seasonal pattern with sl ight lower wind intake during the Summer and the Winter months. The following figure shows the actual observations.
@,91;(&B56&P++1-.&:.1)'1-',0+8&0:&H,+/&,+'-L(&
Source: EnBW (2011), TenneT (2011), 50Hertz (2011), Amprion (2011)
The simulation of the wind power generation in MMEM is real ized by an hourly Monte Carlo simulation modelled upon the actual wind intake and adapted to the predicted capacity levels. To calculate the seasonal distr ibutions, the Palisade software “@Risk” has been used. For each quarter, a statist ical analysis determined via a Chi-Square best-fit algorithm the distr ibution that corresponded most closely to the observations. For the first quarter (January to March), a Gamma distr ibution ranked highest in the Chi-Square analysis . The following graph shows the match between the frequency of observed values (blue bars) and the theoretical distr ibution (red l ine).
64
@,91;(&B=60(;<(/&<-.1(8&-+/&(8',2-'(/&/,8';,41',0+&:0;&J(;,0/&Z-+1-;?&'0&"-;)*&
Source: MMEM (2011)
The observations of the second quarter (Apri l to June) are best represented by a Lognorm distr ibution; the distr ibution has been truncated at Zero, such that unreal ist ic, negative values do not appear.
65
@,91;(&BA60(;<(/&<-.1(8&-+/&(8',2-'(/&/,8';,41',0+&:0;&J(;,0/&PJ;,.&'0&Z1+(&
Source: MMEM (2011)
Like in the previous quarter, the Chi-Square ranking shows for the third quarter (July to September) the best f it with a Lognorm distr ibution. In the input summary statist ics to the right of the graph it can be noted that, l ike in the first quarter, wind feed-in was always above Zero.
@,91;(&BB60(;<(/&<-.1(8&-+/&(8',2-'(/&/,8';,41',0+&:0;&J(;,0/&Z1.?&'0&C(J'(24(;&
Source: MMEM (2011)
The fourth quarter (October to December) fol lows again a Gamma distr ibution.
66
@,91;(&BE60(;<(/&<-.1(8&-+/&(8',2-'(/&/,8';,41',0+&:0;&J(;,0/&#)'04(;&'0&W()(24(;&
Source: MMEM (2011)
During the simulation, subsequent draws of the sampling distr ibutions are autocorrelated by 0.99, according to the autocorrelation of the actual observations. The second fluctuating energy source in the generation mix is solar power. As in the case of wind power, data from the four German grid operators is aggregated according to seasonal patterns. The methodology of translating the feed-in quantit ies is different from the wind pattern, though. Solar radiation occurs in a dai ly cycle with sl ight variat ions, start ing in the morning and ending in the afternoon or evening. Hence, hourly observations have been analysed for each quarter, taking into account that solar intensity changes each quarter. The fol lowing graph shows the averages of hourly photovoltaic energy supply for al l quarters over the 24 hours of a day.
67
Figure&46:&Average&daily&solar&intake&according&to&grid&operator&data&
Source: EnBW (2011), TenneT (2011), 50Hertz (2011), Amprion (2011)
As expected, show quarter 2 and quarter 3 (Apri l to June and July to September, respectively) the highest photovoltaic intake. Since the sun does not shine every day with the same intensity, MMEM uses a Monte Carlo simulation to take dai ly variat ions into account. For the simulation, the standard deviations of each hourly observation per quarter were calculated. They can be interpreted as the l ikel ihood distr ibution for a sunny or cloudy day. The fol lowing figure depicts the standard deviations per quarter.
Figure&47:&Standard&deviations&of&average&daily&solar&intake&according&to&grid&operator&data&
Source: EnBW (2011), TenneT (2011), 50Hertz (2011), Amprion (2011)
While quarters 1 to 3 show a fair ly similar pattern, quarter 4 (October to December) has the most str iking fluctuations. In MMEM, the dai ly solar intake is then modelled as the average hourly value for each quarter with a deviation that is drawn from the Normal Distribution with parameters based on the observed values. Hence, for each day the sunlight is assumed to fluctuate around the hourly mean values with an intensity that corresponds to the Normal Distribution’s divergences.
!
0500
100015002000250030003500400045005000
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Hour
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MW&
Hour&
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Stdev!Q4!
68
6.5. Electricity demand
Similar to the supply side, the demand side is modelled according to real-t ime hourly observations. The observations are provided by UCTE/entso-e, the European network of grid operators, and are adapted to demand predictions of the corresponding scenarios. For example, in the reference case demand is modelled according to the EWI-Prognos forecast. As opposed to the supply side, f luctuations in the demand patterns do not only f luctuate on an hourly basis, but also between weekdays and weekends. In MMEM, for each iteration, i .e . for each month between 2011 and 2050, an entire sample week covering 168 hours is computed. The weeks are differentiated between quarters, because demand in the warmer months Apri l to September is lower than in the colder months. The fol lowing figure shows the weekly trajectories for each quarter.
Figure&48:&Differences&in&the&fluctuations&of&weekly&demand,&according&to&quarters&
Source: UCTE (2009)
In the graph, it can be observed that demand reaches its peak in the middle of the week, Friday has a lower demand than al l other weekdays but shows a similar pattern l ike the other weekdays, whereas Saturdays and Sundays substantial ly differ in quantity. All dai ly patterns fol low the characterist ic morning and late afternoon peak, including the weekends.
6.6. Matching supply and demand: Merit-order dispatch
Electricity markets are characterized by the necessity of instantaneously matching supply and demand. Since demand fluctuates, supply has to fol low the variat ion in order to keep the electricity system in constant equil ibrium. To this end, different technologies are deployed. Their respective deployments depend on technological
0!
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Dem
and&in&German
y&[M
W]&
Sample&Week&[Hours]&
Quarter!1!
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Quarter!4!
69
factors l ike whether switching on and off a power plant is technical ly chal lenging for short intervals of supply, and – especial ly in a l iberal ized market - involves an optimization of the balance between fixed and variable costs. While the fixed costs predominantly reflect init ial capital expenditures, the bulk of the variable costs stems from fuel in the case of fossi l plants, and operation and maintenance in the case of renewable energies l ike solar power and wind. For each level of demand in the dispatch, an optimal technology mix can be identif ied. For the base load, i .e . continuous demand, plants with high start-up costs and low fuel costs, l ike nuclear or l ignite, are suitable, whereas short periods of demand spikes are more efficiently supplied by technologies that exhibit an inverse relat ion between the costs, and can be deployed more flexibly. Hence, the energy supply industry responds with differing technologies to short-term demand variat ions for any given point in t ime on the load curve. Typical ly, generation technologies are put into ascending order along the so-cal led long-run marginal cost curve, such that the cheapest technologies are deployed first , and the most expensive ones last (or not at al l ) . The l ine-up of these technologies corresponds to the merit-order curve. To determine the merit-order curve, cost estimates have been taken from the SRU scenarios, which are based on Nitsch (2009). The fol lowing graph shows the evolution of the long-run marginal costs in cents per kWh for selected technologies. Note that coal deployment with carbon storage and sequestration (CCS) is cheaper than normal coal plants due to increasing costs incurred per ton of carbon dioxide emissions.
Figure&49:&LongIterm&marginal&cost&development&of&selected&technologies&
Source: Nitsch (2009)
This graph only shows a selection of the technologies used in the model, for the fol lowing reason: Some technologies cannot be switched on and off as easi ly as others, they are cal led “must-run” plants. In MMEM, we consider nuclear, hydropower (run-off r iver) and l ignite plants as must-runs: Because their start-up costs are prohibit ively high, they are typical ly located on the merit curve in this sequence (Bundeskartel lamt 2011). On the contrary, due to prospective technological advances, coal plants are shown, because they are more flexible than l ignite or
0!
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Costs&[€Icen
ts/kWh]&
Coal!
CoalCCS!
NaturalGas!
PumpStorage!
Biomass!
Geothermal!
70
nuclear. Given the current German feed-in regulation, the two most prominent renewable energies, solar and wind power, are also considered must-run plants and, as opposed to the other renewable energies l ike geothermal energy or biomass, not deployed according to their l ikely posit ions in the marginal cost curve, but whenever they are avai lable. The fol lowing figure i l lustrates a merit order curve for 2020, as it is used in the model.
Figure&50:&Exemplary&meritIorder&dispatch&curve&for&Germany&in&2020&
Source: MMEM (2011), based on capacity forecast of Prognos-EWI-GWS (2010)
The graph dist inguishes between must-run technologies that are dispatched irrespective of their long-run marginal costs (to the left) , and the l ine-up of al l other technologies according to their costs. Any technology beyond the dotted dispatch l ine, indicating the level of instantaneous demand, is not dispatched. In the Market Model Electric Mobil ity, the merit-order methodology determines for each hour of a sample week in each month of the simulation, which technologies are deployed. The fol lowing graph shows the change in the dispatch for each sample day at 6pm over the simulation period.
0
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NuclearHydropowerLigniteWind OnshoreWind OffshoreSolar Feed-InCoalNatural GasPump StorageGeothermalWasteBiomassFossil Misc
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71
Figure&51:&Generation&deployment&at&6pm&according&to&PrognosIEWIIGWS&capacity&forecasts&
Source: MMEM (2011), based on capacity forecast of Prognos-EWI-GWS (2010)
6.7. Charging patterns of electric vehicles
The most important interaction between electric cars and the electricity sector is the battery charging requirement induced by the distances travel led. The total amount of electricity charged by electric vehicles depends on a range of factors, including
! The!total!number!of!electric!vehicles!! The!driving!pattern,!i.e.!the!amount!of!kilometres!travelled!! The!range!and!efficiency!of!the!engine!technology,!i.e.!pure!battery!electric!vehicle!in!the!compact!
class,!range!extender!SUV!etc.!
The Market Model Electric Mobil ity simulates the behaviour of electric car holders for a sample week of each month from 2011 to 2050, calculated under different power supply conditions for each hour. For this purpose, data from the "Mobil ity in Germany (2008)" is extracted, under the simplifying but not ful ly unreal ist ic assumption that car holders do not change their travel behaviour irrespective of the type of car and technology they choose. Hence, they are assumed to purchase a car that satisf ies their individual travel patterns. The 2008 dataset of Mobil ity in Germany (Mobil ität in Deutschland) provides a detai led overview of how many cars are actual ly used during a day of the week (numbers drop drastical ly for the weekend). In addit ion, i t provides exact information to distance, start and end of the tr ip, i ts purpose (commuting to work, shopping, leisure activit ies) , and whether it is a “loop” or “leg” in the terminology of transport economists, i .e . a round trip for instance to work and back at home or a tr ip with multiple stops, including e.g. shopping activit ies, and the t ime spent in the car. The information can be used to construct an exact representation of how the vehicle stock is used. The following graph shows the result of the analysis .
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Figure&52:&Weekly&mobility&patterns&of&private&car&owners&
Source: MMEM (2011), based on MiD (2008)
As the graph shows, more than half of the cars are – on average – not used; in addit ion, a significant share of car holders leaves the car at work during day-time. During the weekends, the share of cars not used increases to more than 70 percent. Each car in the dataset contains several observations about each tr ip’s length, t iming and destination. In the market model, the observations are aggregated to determine the total amount of ki lometres travel led per day by each car. The fol lowing graph shows the histogram, differentiated according to weekdays, Saturdays and Sundays, both in terms of actual observations and of cumulative percentages. For weekdays, 8964 val id observations were avai lable, for Saturdays and Sundays 1500 and 1115 observations, respectively.
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Figure&53:&Histograms&for&total&daily&trip&length,&split&according&to&weekday,&Saturday&and&Sunday&
Source: MMEM (2011), based on MiD (2008)
As the graph shows, the cumulative percentages for weekdays and weekends do not differ substantial ly; approximately 80 percent of dai ly tr ips were below 70 ki lometres. For the charging behaviour, the Market Model Electric Mobil i ty then differentiates between two main scenarios:
! Electric vehicle owners charge exclusively in the evening at home after returning from their daily trips
! Electric vehicle owners charge as soon as they either return home, i.e. also during day-time, as well as
at their work place, if the purpose of their trip was commuting to work
! In none of the scenarios electric vehicle holders charge while their cars is parked during leisure or
shopping activities
These two charging patterns induce different aggregated load profi les, because the second option al lows for larger t ime spans of coordinated charging. If travel distances of pure battery electric vehicles exceed their l imited battery range, they are assumed to charge at quick charging stations on the road. This charging requirement occurs in regular t ime intervals, depending on the range of the vehicles, but shifts dynamical ly according to improvements in battery technology. Range extenders and plug-in hybrids are assumed to use their ful l electric range before switching to the addit ional gas intake. Total power charging requirements are hence sl ightly larger in the second scenario, because car holders may recharge their batteries at work or at home during day-time. The following graph shows for an exemplary week in 2020 the aggregated charging behaviour of owners of pure battery electric vehicles.
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Figure&54:&Aggregated&charging&requirements&of&pure&battery&electric&vehicle,&sample&week&2020&
Source: MMEM (2011)
It can be observed that the bulk of charging requirements of pure battery electric vehicles occurs at home, which in turn negatively affects the profitabil i ty of quick charging stations.
6.8. Coordinated and uncoordinated charging
The reference scenario of MMEM assumes that car holders charge their vehicles according to their own preferences, i .e . at the point in t ime when they decide it to be appropriate or necessary. However, one of the benefits of the storage capacit ies of electric batteries is that they can capture power when excess supply generated by renewable energies l ike wind or solar is potential ly avai lable, but cannot be used in the system (for example, when overal l demand is low and is exclusively met by must-run plants l ike nuclear or l ignite) . During these periods, the renewable energy would be lost. Electric vehicles can hence play a role in balancing the system towards greater efficiency and resource use. Due to the l imited capacit ies of the batteries, the overal l contribution is l ikely to be l imited, at least in the coming decades, but nonetheless has to be quantif ied. In addition, coordination in the charging patterns of electric vehicle owners may become necessary when the low and medium voltage grid is no longer capable of supporting a joint charging behaviour within a geographic area. Technical restrict ions, which wil l be discussed in detai l later, may induce individual parts of the equipment, l ike transformers in the low and medium voltage grid, to fai l . Coordinated charging switches parts of the load to later points in t ime, such that crit ical technical thresholds of the grid are not exceeded.
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The Market Model Electric Mobil ity assumes that coordinated charging is technical ly feasible, i .e . the necessary information and communications technology is instal led together with a smart metering system. In the reference scenario, car holders have to pay for the addit ional investments. The extension of the low and medium voltage grid, however, has to be financed by the network operators. The algorithms implemented in the simulation use the monthly sample week and the corresponding charging requirements to optimise charging in the fol lowing way: Charging is coordinated to reduce peak charging as much as feasible, given the constraint that al l batteries have to be ful ly recharged unti l the next morning at 6 am, or when commuters leave their workplace and drive home. The following graph shows the effect of the load shift ing for an exemplary week in 2020 for al l three technologies, pure battery electric vehicles, range extenders and plug-in hybrids in the reference scenario. The blue l ine indicates the load when charging is uncoordinated, whereas the red l ine assumes coordinated charging.
Figure&55:&Difference&in&EV&loads&due&to&uncoordinated&and&coordinated&charging&
Source: MMEM (2011)
Most importantly, the load shift cuts demand of electric vehicles by around 15 per cent in the evening peak hours, i .e . during the t ime of the day when car holders typical ly arrive at home and plug their vehicle. Consequently, more charging is delayed unti l the late night hours, mainly between 3am and 6 am, when in the uncoordinated scenario most of the charging has already been accomplished. As mentioned above, an equally important objective of coordinated charging is the shift ing of load to periods of excess renewable energies. The merit order dispatch simulated in the Market Model Electric Mobil ity provides hourly information on wind supply beyond demand. Given that wind and solar are considered must-run technologies due to the feed-in regulation in Germany, excess wind power occurs only when the combination of al l other must-run technologies, including nuclear, l ignite, run-off r iver hydropower and photovoltaics, exceeds overal l demand. During those hours, a l imited quantity of home or workplace charging may be shifted. While on-the-road charging of pure battery electric vehicles is completely
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inelastic, the MMEM reference scenario al lows for load shifts after 6pm and before 6am, when al l batteries have to be ful ly charged. A larger t ime slot is assumed in the alternative scenario. It supposes that car holders also charge their vehicles during day-time, when they are at home, or at their work place, in a coordinated way. Thus, load shift ing can be optimised especial ly for hours during the day when the reference scenario assumes that the cars used for commuting are general ly not plugged in.
6.9. Non-tailpipe emissions
MMEM provides a dynamic, endogenous prediction of tai lpipe emissions of combustion vehicles and mild hybrids, based on the optimisation efforts of car manufacturers. The model also attempts to quantify non-tai lpipe emissions of electric vehicles. In general , the potential of this technology echoes the cl imate change debate and it appears appeal ing as an option to reduce CO2 emissions. This expectation however needs to be tested against a real ist ic calculation of the CO2 impact of electric vehicles. While the electric powertrain, by definit ion, induces Zero tai lpipe emissions, the energy that is used by electric cars is supplied by power plants, which – in most electricity systems – consist of a mixture between emission-free sources of energy, e.g. wind or solar power, and fossi l-based plants that release greenhouse gases while in operation. Calculations of these non-tai lpipe emissions pose a methodological chal lenge, because the physics of a meshed, multi -nodal electrical network, fol lowing the so-cal led Kirchhoff’s law, do not al low for actively directing an electron’s path from a source to a specified destination. Suggestions for overcoming this methodological obstacle include assigning the average pollution of the generation structure to EVs, or the emissions of the peak average plant. These approaches are frequently mentioned in the polit ical debate, but may be misleading, as wil l be i l lustrated below in detai l , for a proper assessment of the cl imate impact of electromobil ity.
6.9.1. Literature overview
Consistent with the growing relevance of electromobil ity on the policy agenda, a significant number of academic papers and studies from think tanks and state-sponsored institutes on the effect of electric vehicles on CO2 emissions have been published in the most recent years. The research can broadly be classif ied in two categories: f irst , research that merely quantif ies the effects on emissions of EV diffusion; second, research that adds cost considerations to this, and thus estimates the unit cost of CO2 abatement through EV deployment. The majority of publications on the topic belongs to the first stream of research. Table 56 introduces some among these recent contributions. The general message that emerges from these studies is that CO2 emissions can be reduced through EV diffusion. We find, however, that there are some l imitations in these results. First , the majority of the studies concentrate on plug-in hybrids, whereas much fewer results are avai lable for pure battery electric vehicles. Second, most of the studies use an exogenous assumption for electric vehicle diffusion (see column “diffusion”), i .e . the market uptake does not depend on consumer choice modeling. While this could, to a certain extent, be neglected when looking at unit (CO2 saving per vehicle) results, i t certainly casts doubts on any quantif ication of the aggregate savings of al l newly registered electric vehicles. Third, for the estimation of non-tai lpipe emissions
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most of the results make use of the average CO2 content of the electricity generation (see column “CO2 intensity”). We consider this assumption as heavily influential on the results and we see it as discussable. Other studies relate the CO2 content to the emissions of the peak marginal t e chnology , i .e . the power plant that del ivers the energy to the ult imate unit of addit ional demand. This is a proposal that seems as well questionable to us, as wil l be i l lustrated below. Eventually, a last set of studies makes use of a detai led and disaggregated simulation of the electric sector to determine CO2
emissions. This latest possibi l i ty, provided the underlying simulation method is well-documented and offers sufficient scientif ic credentials , appears suitable; i t comes however at a high price in terms of data requirements and is heavily resource consuming, which gives room to the definit ion of more parsimonious methods. Additionally, in most of the studies emissions are found to be non-responsive to policy (column “policy responsive”). This can be a hindrance for the general need to assess the effect of given policies on emissions. Moreover, one can observe that most of the exist ing results rely on a load of the energy sector that has no specific t ime pattern (column “Time pattern”). This appears to us as a l imitation as well , considering the expectation that EV reloading may have a specific t ime pattern and some policies may specif ical ly be targeted to alter this t ime pattern. Additional to these contributions, some authors have gone one step beyond in their analysis by investigating the cost efficiency of EV diffusion as a means to reduce CO2 emissions. (Kammen 2009)) estimates the cost of CO2 reduction as the CO2 abatement divided by the monetary incentive that would ( in our wording) make alternative vehicles cost equivalent to conventional vehicles. Their main finding is that “(without af fordable batter i es) GHG emiss ion reduct ions from PHEV’s cost wel l over 100$/t.CO2eq”, leading to the conclusion that “PHEV are not current ly a cost e f f e c t ive means o f mit igat ing GHG’s”. (de Boncourt 2011)) bases the cost of CO2 abatement in France on a 2 bi l l ion € policy package for 2 mil l ion vehicles. The cost of this policy is then divided by the result ing reduction in emissions in order to obtain the unit cost of CO2 reduction. Based on the assumption that cars circulate on average 150,000 km the author calculates a cost of 50€/t CO2. Study Diffusion Co2
intensity Policy responsive
Time pattern
Key finding
(Brady and O'Mahony 2011)
EV. Exogeneous diffusion assumption -90% penetration by 2035.
Average CO2 mix.
No No Even with large penetration CO2
gains are modest compared with the size of national CO2
emissions. (Doucette and McCulloch 2011)
BEV. Exogeneous diffusion assumption
Average CO2 mix
No No In countries with high CO2
energy production sector, shift to BEV may increase emissions.
(Göransson, Karlsson et al . 2009)
PHEV so as to reprensent
Based on simulation
Yes. With different coordination
Coordination can drastical ly change
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3, 12 or 20 % of electric consumption
policy. emissions from PHEV.
(Thiel , Perujo et al . 2010)
PHEV, EV, BEV
Average mix
No No CO2 abatment can be effective but at an high cost (> 800€/t. for BEV)
(EPRI and NRDC 2007)
PHEV Marginal Natural Gas
(Smith 2010)
PHEV. Exogeneous assumption
No No Up to 50 % reduction in CO2/km compared with other technologies.
(Kyle and Kim 2011)
EV (and Hydrogen). Exogeneous
Based on CGE simulation
Responsive to carbon pricing policy.
No Reduction of emissions due to emission pricing are larger in scenarios with large share of EV.
Table&56:&Electric&vehicle&nonItailpipe&emissions&across&various&studies&
Source: MMEM (2011)
These figures cast doubt on the cost effectiveness of electric vehicles as a tool to reduce CO2 emissions, but cannot in
themselves be found conclusive. One reason among others is that these ratios have a limited representation of micro-
economic, behavioral assumptions: the response of the purchasers is not functionally linked to the incentive. For
instance, no empirical findings are presented to support the hypothesis that cost equivalence between electric and
conventional vehicles makes a fraction of people switch from one to the other.
6.9.2. Methods for computation of CO2 emissions
Before turning towards different approaches for the estimation of non-tai lpipe emission, i t should be emphasized that the notion of addit ional emissions is in itself problematic. Within the current framework of EU cl imate policy and regulation, the energy sector is part of the European Emission Trading System (EU ETS). This means that its emissions are intrinsical ly capped. By contrast, car use emissions are not regulated, and neither is intended to be in a foreseeable future. Hence, electromobil ity shifts energy demand from a non-capped sector to a capped sector. This intrinsical ly annihilates the emissions of the vehicle, as underl ined by (Hacker, Harthan et al . 2009) and (Horst, Frey et al . 2009). While this perspective is consistent and grasps, to our view, the ult imate underlying mechanisms of emissions regulations, we argue that there are st i l l some val id reasons in quantifying emissions related to electric cars. A weak objection is that electric vehicle diffusion wil l direct some output of the energy sector to electric vehicle in substitution to other use, so the question of electric vehicle emissions is st i l l relevant. A more fundamental argument is that this substitution comes at some social cost, as i t typical ly excludes
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some other (CO2 emitt ing) social needs to be served by the energy sector. This implies that omitting the CO2 emissions related to electric vehicle diffusion would distort the assessment of the total costs or benefits of electromobil ity from a welfare point of view. Rather, relying on the preferences embodied in the policy maker’s uti l i ty function, one can use the marginal cost of electromobil ity-related CO2
emissions as a measure of the other CO2 emitt ing uses foregone. As introduced before, the dominating computation method for CO2 emissions rel ies on average emissions and marginal (to be understood as peak marginal) emissions. Average emissions can be expressed as
!!,!.!!,!!!!!
!!,!!!!!
(10) With k, energy generation technology, y year considered, ek , y emission factor of a given technology in a given year, and Qk , y quantity of energy produced with a given technology in a given year. In some cases, these ratios are computed net of “must-run” faci l i t ies, such as in:
!!,!.!!,!!∈!!!!,!!∈!!
! ! ! !
(11) where K1 is a subset of technologies that excludes must-run generation. Peak Marginal emissions are just the emissions of the technology that is activated to meet demand in peak hours. These two methods can deliver fundamental ly different results. (Bettle, Pout et al . 2006) (as quoted by (Doucette and McCulloch 2011)) point out that using average CO2 emissions rather than Marginal Emissions Factors can underestimate emissions by up to 50 %. However, most of the users of average emission do not comment on the reasons for preferring this method over others. The approach bears a major practical advantage, though: Data on average emissions is often easi ly avai lable. For instance, the German Umweltbundesamt provides historical records of specif ic CO2
emissions per Megawatt hour of the German generation mix (see (UBA 2011)). A legit imate conjecture is that the average mix is only suitable as a f irst approximation. By contrast, supporters of the peak marginal approach provide reasons for their method arguing, for instance, that “PHEV represent a
new electricity demand and consume electricity produced by the marginal plant. In the short run it is incorrect to calculate the
environmental impacts of PHEV’s using average electricity emissions” ((Kammen 2009), see as well (Carlsson and Johansson-Stenman 2003)). While this statement is, prima facie, acceptable as long as it makes explicit that it deals with short-term fluctuations of
demand, it becomes discussible when the additional electricity demand stemming from electromobility is a recurrent and fairly predictable
one. Individual car owners may follow different charging routines – similar to households using electricity in slight timely variations – but
on the aggregate level the load profile will be highly predictable, especially because of the slow uptake, which allows grid operators and
utilities to observe charging behaviors and anticipate the required additional demand. Moreover, one can doubt on how peak marginal
approach takes realistically into account the peculiar time pattern (relating to the distribution of demand across hours, across
working/non-working days, and across seasons) of additional electricity consumption of electric cars. Implicitly, it makes use of the
“peak” additional plant, an assumption that becomes difficult to sustain considering that large part of the additional electricity request
from EV that may add on non-peak demand.
Historical marginal emissions are based on the real world data reflecting how, in power sector operations, emissions change with
electricity output ((Hawkes 2010)). Based on time series, indicating with a fairly high time resolution (typically 15 to 60 min) the dispatch
status of single plants and their respective emissions, one can obtain a vector of observed marginal emissions per kWh:
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( !!.!!,!)!∈! !( !!.!!,!!!)!∈!( !!,!)!∈! !( !!,!!!)!∈!
(12) ! where ek,t refers to the emissions of plant operating with technology k at time t, and Qk,t refers to the production with technology k for a number of t periods (for simplification we omit the notation for each single plant). The main advantage of this method is that it is fully consistent with current operating practices. This advantage however turns to a limitation when one wants to apply the method to emissions in other countries and, most importantly in future years, when operating conditions of the energy system may have significantly changed.
Long term marginal emissions – sometimes referred to as “built marginal” – overcome parts of these limitations in considering that the energy system can respond to a change in the demand pattern by adjusting the installed power plants. This method often encounters some skepticism due to the “marginal” (meaning “low magnitude”) nature of the demand. (Bradley and Frank 2009), for instance, claim that even in scenarios assuming a very large diffusion of PHEV, they “could be serviced using the present generation and transmission capacity of the US electrical grid”. Such a statement does however not invalidate the idea that this additional demand may alter the optimum (profit maximizing) mix of energy plants. Thus it is conceptually correct to analyze the impact of a recurrent demand using the built marginal approach. The difficulty is then to identify the marginal “built” technology.
One solution is to look at historical data what French l i terature designates as “marge récemment construite”. For instance: “the build marginal is […] generation weighted average emission factor of the service power units that have been built most recently”((AECOM 2010)). It can be based on
!!!! !!!! . !!,!!!!!,!!!!
!!!!!!! !!,!!!!!,!!!!
!!!
(13) where i refers to backward years that are taken into account in the calculation, while Kk refers to the instal led capacity of each technology, and �k integrates the fact that only a fraction of instal led capacity actual ly produces energy in a given year (this relates to maintenance operations and to faci l i t ies not operating at ful l capacity) . The l imitation of the approach is that such an “historical” built marginal may not reflect possible changes in the energy policy of a country. This l imitation can be overcome, though, by taking into account real ist ic assumptions about the size (and operating rate) of future news plants that are programmed for each technology in the relevant t ime horizon, thereby giving rise to a forward built marginal approach. The implementation of this approach may rise some discussions in contexts where the overal l domestic energy consumption is anticipated to shrink in the future years (see e.g. (Prognos-EWI-GWS 2010)), a phenomenon that relates to demographic decl ine, increased efficiency, and a shift from energy-consuming industries to services. In this context, one could claim that built marginal reflects substitution rather than expansion mechanisms. It is however fair to say that planned construction provides consistent information on which technology are l ikely to be expanded in case of addit ional demand. Two other methods for computing emissions can be used. Electric sector simulation models, offer a detai led description of how the energy system responds at the plant (or group of plants) level , to changes in the demand. Provided their dynamic is
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suitably documented and has received sufficient scientif ic scrutiny, and provided that they can real ist ical ly replicate both investment and operating decisions of the energy sector, these models, such as NEMS or IPM, can yield valuable insights into the evolution of CO2 emissions in response to addit ional electricity consumption. As previously stated, the main l imitation of this method is however that it is very data intensive and resource consuming. A comprehensive view of the different avai lable techniques is provided in the fol lowing table, which also provides some values of emissions for Germany making use of these methods. Apart from “average mix” and “marginal (peak)” some other methods that are considered in the l i terature wil l be presented briefly.
Methodology Example (Countries in Brackets)
! Short term marginal
! (operating marginal)
(Carlsson and Johansson-Stenman 2003) (S)
(Pöyry 2010) (BE, D, DK)
! peak marginal emissions (de Boncourt 2011), (F)
(Kammen 2009) (US)
! “historical” marginal emissions (Hawkes 2010) (UK)
! Long term (built) marginal
! Forward built (Pehnt, Höpfner et al. 2007), assuming 770 g/kwh to 840g/kwh, corresponding to emission estimates of planned coal plants (D)
! based on historical long term marginal emissions
(Market Transformation Programme 2009) (UK)
! Pivotal emissions Inexisting to our best knowledge
! Simulation based on simulated technology mix.
(Göransson, Karlsson et al. 2009)
! Average emissions (Baum, Dobberstein et al. 2011), p. 17, assuming 533 g/kwh in 2010 and 330 g/kwh in 2020 (D)
(Horst, Frey et al. 2009), assuming 625 gCO2/kWh (D)
(UBA 2011), estimating 565 g/kWh in 2010 (D)
Table&57:&Methods&used&for&emission&calculations&of&EV&
Source: MMEM (2011)
Eventual ly, we present one addit ional method that we consider appropriate for a r igorous computation of addit ional CO2 emissions. This method, that can be labeled as Pivotal Marginal (or alternatively “hourly marginal”) , intrinsical ly acknowledges the fact that addit ional electricity request due to EV deployment has a peculiar t ime pattern that needs to be accounted for in the calculation. Focusing on hourly distr ibution of this addit ional demand, one can consider that the technology used to respond to a change in the demand is not identical across hours. The following figure represents the impact of an addit ional demand on a pre-exist ing load profi le and replicates how various technologies are operated, based on their merit order, to respond to variations in demand. In the short run, i t is legit imate to consider that an addit ional demand in a given t ime slot wil l be met by activating the “pivotal” technology that corresponds to this t ime slot, meaning the technology that on the merit order curve feeds the marginal demand. Over the long term, the question of which addit ional plant could be built due to this addit ional demand is more complex. One can however observe that the merit order curve, and the corresponding definit ion of the pivotal technology for each level of output, inherently represents the optimal technology for each layer of the demand. Thus the
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pivotal technology shal l not only be seen as informative of operating conditions but as well of investment conditions. For this reason, i t is compatible both with short term and long term adjustment mechanisms to assign any addit ional demand to the technology that is pivotal for that range of demand in the forecasted energy supply curve.
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Figure&58:&Simplified&representation&of&pivotal&technologies&and&additional&demand&
Source: MMEM (2011)
The methodology expands easi ly to take into account different types of dai ly loads, e.g. weekdays compared to Saturdays and Sundays, and different seasons. Additionally, this demand can easi ly integrate “excess supply” of renewables that may be avai lable in certain t ime periods. This can be accomplished by deducting this “renewables’ excess supply” from the addit ional EV demand. Moreover, the computation can also be combined with the Monte Carlo simulation applied in MMEM to represent the distr ibution of renewables.
6.9.3. Computation of CO2 emissions
For the quantif ication of non-tai lpipe emissions of electric cars we use the generation portfolio and demand predictions of the EWI-Prognos reference scenario. In sum, we calculate six different charging alternatives:
1) Uncoordinated!charging!between!6pm!and!6am!2) Coordinated!charging!between!6pm!and!6am!3) Uncoordinated!charging!when!car!holders!arrive!at!work!or!as!soon!as!they!come!home!4) Coordinated!charging!when!car!holders!arrive!at!work!or!as!soon!as!they!come!home!5) Uncoordinated&charging&with&the&option&to&use&decentralized&photovoltaic&supply,! i.e.!whenever!
solar! radiation! exceeds! a! minimum! threshold! and! the! electric! vehicle! is! plugged! at! work! or! at!home,!the!photovoltaic!energy!is!not!fed!into!the!central!grid!but!used!for!the!car;!when!no!solar!feedEin!is!available,!e.g.!at!night,!electricity!is!supplied!from!the!grid!!
6) Coordinated& charging& with& the& option& to& use& decentralized& photovoltaic& supply,! similar! to!alternative! 5,! but! when! no! solar! feedEin! is! available,! e.g.! at! night,! electricity! is! supplied! via! a!coordinated!mechanism!from!the!grid.!i.e.!whenever!solar!radiation!exceeds!a!minimum!threshold!and!the!electric!vehicle!is!plugged!at!work!or!at!home,!the!photovoltaic!energy!is!not!fed!into!the!central!grid!but!used!for!the!car!!
Alternatives 5 and 6 represent a policy option to decrease at least parts of the central energy supply and decrease the technical burden of the medium and low voltage grid. However, this option comes at high opportunity costs – as long as private owners of
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photovoltaic panels are compensated by German feed-in regulation, they have to be compensated for each kWh not del ivered to the local supplier. MMEM applies the charging patterns based on the MiD analysis to the merit-order dispatch model and determines the pivotal technology that supplies the electricity according to the charging requirements of the electric vehicles. For the reference scenario, the fol lowing graph shows the shares of the different shares in the overal l supply.
Figure&59:&Pivotal&technologies&share&in&the&MMEM&reference&scenario&with&EWIIPrognos&portfolio&
Source: MMEM (2011)
Until 2020, coal power plants wil l be the dominant supplier of energy for electric vehicles. The share decl ines steadily but slowly, unti l natural gas plants emerge. By the mid-2020s, natural gas takes over and remains the main technology, reaching levels of over 90 percent before 2030. Renewable energies wil l play a negligible role in the provision of electricity for electromobil ity. According to the model, onshore wind wil l account for less than 10 percent of the supply before 2020. After 2035, pump storage wil l capture an increasing share of the charging demand. Pump storage, however, is not a primary energy by itself , but uses avai lable electricity to pump the water. In the case that pump storage wil l exclusively be fuel led by excess renewable energies, pump storage can be considered a Zero emission technology. Towards the end of the simulation, biomass wil l capture a small share of the generation mix. This f inding has important implications for the average non-tai lpipe emissions of electric vehicles. Given the charging requirements according to the six alternative charging scenarios presented above, the average emissions per ki lometer can be as low as 37 g CO2/km in 2020, or as high as 118 g CO2/km, well above the 95 g CO2/km imposed by the EU Regulation 443.
0%!10%!20%!30%!40%!50%!60%!70%!80%!90%!
100%!
1/1/2012!
6/1/2013!
11/1/2014!
4/1/2016!
9/1/2017!
2/1/2019!
7/1/2020!
12/1/2021!
5/1/2023!
10/1/2024!
3/1/2026!
8/1/2027!
1/1/2029!
6/1/2030!
11/1/2031!
4/1/2033!
9/1/2034!
2/1/2036!
7/1/2037!
12/1/2038!
5/1/2040!
10/1/2041!
3/1/2043!
8/1/2044!
1/1/2046!
6/1/2047!
11/1/2048!
Nuclear! Coal! Coal!CCS! Lignite!
Lignite!CCS! Natural!Gas! Imports! Pump!Storage!
Fossil!Misc!(Oil)! Hydropower! Wind!Onshore! Wind!Offshore!
Biomass! Photovoltaics! Geothermal! Renew!Misc!(Waste)!
85
Figure& 60:& Specific& emissions& of& electric& vehicles& in& the& MMEM& reference& scenario,& calculated& for& 6& charging&
alternatives&
Source: MMEM (2011)
The major reason for the high levels of carbon dioxide emissions in the reference scenario (alternative 1) l ies in the fact that the emission intensity of coal is high, and coal wil l remain the major source of power for electric vehicles. Specific emissions can be reduced, though, by a maximum of around 10 g CO2/km if the charging is coordinated and occurs not only at night, but also during day-time at home and at work. The graph shows that the benefits of coordinated charging are l imited over the t ime span between 2020 and 2040. If natural gas is the predominant supply technology, shift ing loads may help to reduce low and medium voltage grid bottlenecks, but it does not lead to a lower emission level . Only alternatives 5 and 6 show a significantly better cl imate performance, but they come – as previously mentioned - at the high cost of compensating panel owners for their lost revenues. The marginal posit ion of renewables in the supply mix is not fundamental ly different for the second electricity forecast, the SRU scenario.
0!20!40!60!80!100!120!140!160!
1/1/2012!
7/1/2013!
1/1/2015!
7/1/2016!
1/1/2018!
7/1/2019!
1/1/2021!
7/1/2022!
1/1/2024!
7/1/2025!
1/1/2027!
7/1/2028!
1/1/2030!
7/1/2031!
1/1/2033!
7/1/2034!
1/1/2036!
7/1/2037!
1/1/2039!
7/1/2040!
1/1/2042!
7/1/2043!
1/1/2045!
7/1/2046!
1/1/2048!
7/1/2049!
g&CO
2/km
&
Uncoordinated!charging!between!6pm!and!6am!
Coordinated!charging!between!6pm!and!6am!
Uncoordinated!charging!at!work!or!at!home!
Coordinated!charging!at!work!or!at!home!
Uncoordinated!charging!with!decentralised!solar!power!
Coordinated!charging!with!decentralised!solar!power!
86
Figure&61:&Pivotal&technologies&share&in&the&SRU&509&TWh&scenario&
Source: MMEM (2011)
Although the share of coal decl ines more steeply, from 2020 onwards gas wil l remain the dominating source of primary energy over the simulation horizon. The technology’s specific emissions are lower than those of coal plants, but can of course not be considered emission-free. Some share of energy demand is satisfied by on-shore and off-shore wind, and later by biomass. However, from 2030 energy has to be imported to satisfy the addit ional demand. If this supply stems from large solar thermal plants l ike Desertec or off-shore wind farms or hydropower in Scandinavia, the average emissions can be reduced to a certain extent. If they are assumed to correspond to the average emission mix of the European power generation portfol io, as it is shown in the graph, then emissions may even rise again. However, the benefits of coordinated charging at home or at work in the next decade amount to approximately 20 g CO2/km, and al l s ix alternative charging modes would comply with Regulation 443, if electric vehicles were not exempted.
0%!10%!20%!30%!40%!50%!60%!70%!80%!90%!
100%!1/1/2012!
6/1/2013!
11/1/2014!
4/1/2016!
9/1/2017!
2/1/2019!
7/1/2020!
12/1/2021!
5/1/2023!
10/1/2024!
3/1/2026!
8/1/2027!
1/1/2029!
6/1/2030!
11/1/2031!
4/1/2033!
9/1/2034!
2/1/2036!
7/1/2037!
12/1/2038!
5/1/2040!
10/1/2041!
3/1/2043!
8/1/2044!
1/1/2046!
6/1/2047!
11/1/2048!
Nuclear! Coal! Coal!CCS! Lignite!
Lignite!CCS! Natural!Gas! Imports! Pump!Storage!
Fossil!Misc!(Oil)! Hydropower! Wind!Onshore! Wind!Offshore!
Biomass! Photovoltaics! Geothermal! Renew!Misc!(Waste)!
87
Figure&62:&Specific&emissions&of&electric&vehicles&according&to&the&SRU&509&TWh&scenario,&calculated&for&6&charging&
alternatives&
Source: MMEM (2011)
The MMEM scenario of 100 percent renewable energy supply in the near future provides the most optimistic outlook concerning the non-tai lpipe emissions of electric vehicles. Given the diverse structure of the supply mix, coal and natural gas would account for approximately a third of al l electricity supplied in 2020, with their shares further decl ining and being replaced by emission-free coal plants with addit ional carbon storage and sequestration. Wind on-shore and off-shore, biomass, waste and later photovoltaics would del iver the electricity for the vehicles.
0!
20!
40!
60!
80!
100!
120!
140!
1/1/2012!
7/1/2013!
1/1/2015!
7/1/2016!
1/1/2018!
7/1/2019!
1/1/2021!
7/1/2022!
1/1/2024!
7/1/2025!
1/1/2027!
7/1/2028!
1/1/2030!
7/1/2031!
1/1/2033!
7/1/2034!
1/1/2036!
7/1/2037!
1/1/2039!
7/1/2040!
1/1/2042!
7/1/2043!
1/1/2045!
7/1/2046!
1/1/2048!
7/1/2049!
g&CO
2/km
&
Uncoordinated!charging!between!6pm!and!6am!
Coordinated!charging!between!6pm!and!6am!
Uncoordinated!charging!at!work!or!at!home!
Coordinated!charging!at!work!or!at!home!
Uncoordinated!charging!with!decentralised!solar!power!
Coordinated!charging!with!decentralised!solar!power!
88
Figure&63:&Pivotal&technologies&share&in&the&MMEM&100&%&renewables&scenario&
Source: MMEM (2011)
The supply mix of this scenario ful ly corresponds to the low-carbon path suggested by polit icians. However, i t would also come at substantial opportunity costs if emission certif icates for coal and natural gas plants do not increase significantly in price. Figure: Specif ic emissions of electric vehicles according to the MMEM 100 % renewables scenario, calculated for 6 charging alternatives Figure&64:&Pivotal&technologies&share&in&the&MMEM&100&%&renewables&scenario&
Source: MMEM (2011)
Although the scenario portrays the most optimistic case in terms of specific emissions of electric vehicles, i t seems basical ly impossible to completely el iminate power plants based on fossi l resource from the supply mix.
0%!10%!20%!30%!40%!50%!60%!70%!80%!90%!
100%!1/1/2012!
6/1/2013!
11/1/2014!
4/1/2016!
9/1/2017!
2/1/2019!
7/1/2020!
12/1/2021!
5/1/2023!
10/1/2024!
3/1/2026!
8/1/2027!
1/1/2029!
6/1/2030!
11/1/2031!
4/1/2033!
9/1/2034!
2/1/2036!
7/1/2037!
12/1/2038!
5/1/2040!
10/1/2041!
3/1/2043!
8/1/2044!
1/1/2046!
6/1/2047!
11/1/2048!
Nuclear! Coal! Coal!CCS! Lignite!
Lignite!CCS! Natural!Gas! Imports! Pump!Storage!
Fossil!Misc!(Oil)! Hydropower! Wind!Onshore! Wind!Offshore!
Biomass! Photovoltaics! Geothermal! Renew!Misc!(Waste)!
89
Figure&65:&Specific&emissions&of&electric&vehicles&according&to&the&MMEM&100&%&renewables&scenario,&calculated&for&
6&charging&alternatives&
Source: MMEM (2011)
At this point we have to recal l that the integration of transport emissions into the European trading mechanism leads by definit ion to a reduction of emissions, because the overal l emissions of the electricity sector are capped. A more detai led analysis of the shadow costs associated to the integration may be necessary. In particular, electromobil ity may increase the overal l prices of trading certif icates due to greater scarcity rents.
6.9.4. Power consumption and grid investments
The charging requirements of electric vehicles can be calculated from the amount of electric vehicles, their specific
consumption, and the total mileage. Given the calculations on the weekly driving patterns and the diffusion of
electric vehicles across different segments and technologies, MMEM can determine how much electricity demand
stems from electromobility at any point in time along the simulation period. Given the overall demand predictions of
the reference scenario based on Prognos-EWI-GWS, we can calculate the share of electric vehicle demand in overall
domestic electricity demand.
0!
20!
40!
60!
80!
100!
120!
140!
1/1/2012!
7/1/2013!
1/1/2015!
7/1/2016!
1/1/2018!
7/1/2019!
1/1/2021!
7/1/2022!
1/1/2024!
7/1/2025!
1/1/2027!
7/1/2028!
1/1/2030!
7/1/2031!
1/1/2033!
7/1/2034!
1/1/2036!
7/1/2037!
1/1/2039!
7/1/2040!
1/1/2042!
7/1/2043!
1/1/2045!
7/1/2046!
1/1/2048!
7/1/2049!
g&CO
2/km
&
Uncoordinated!charging!between!6pm!and!6am!
Coordinated!charging!between!6pm!and!6am!
Uncoordinated!charging!at!work!or!at!home!
Coordinated!charging!at!work!or!at!home!
Uncoordinated!charging!with!decentralised!solar!power!
Coordinated!charging!with!decentralised!solar!power!
90
Figure&66:&Share&of&electric&vehicle&demand&in&overall&electricity&demand&in&Germany&
Source: MMEM (2011)
The figure clearly shows that the impact of electric vehicles on the overal l electricity demand wil l be fair ly l imited. Even towards the end of the simulation horizon EV charging wil l not exceed 1.2 percent of the total demand in the reference scenario. This is mainly due to three factors: electric motors are already more efficient than combustion engines, in the future they wil l further increase their efficiency; the overal l share of electric vehicles wil l be l imited; range extenders and plug-in hybrids wil l continue to use gas for a part of their fuel requirements. The low level of energy consumption of electric vehicles could lead to the conclusion that no grid investments wil l be necessary. However, once that a larger share of car holders has purchased an electric vehicle, the peculiar charging characterist ics of that technology wil l impose the necessity to reinforce the low and medium voltage grid: Although the batteries of electric cars only store minimal amounts of electricity compared to the energy content of other forms of mechanical , chemical or kinetic storage, they require the electricity to be inserted into the battery at “high pressure”. When rapid charging is supposed to take place in residential areas, the investment needs wil l become even larger. MMEM does not calculate the potential fai lures of the low and medium voltage grid, but rel ies on a meta-analysis of independent investigations by RWE, E. ON, the RWTH Aachen and the University of Magdeburg, which have detai led, disaggregated representations of low-voltage grid clusters. All studies have in common that they determine a density of electric vehicles, at which a combined charging pattern would lead to a l ikely fai lure of individual system components. The meta-analysis uses these technical benchmarks to determine a functional relat ionship between the density of electric vehicle penetration as a share of total vehicle stock and combines result from the studies to determine the l ikel ihood of system fai lures.
0.0%!
0.2%!
0.4%!
0.6%!
0.8%!
1.0%!
1.2%!01/01/11!
01/08/12!
01/03/14!
01/10/15!
01/05/17!
01/12/18!
01/07/20!
01/02/22!
01/09/23!
01/04/25!
01/11/26!
01/06/28!
01/01/30!
01/08/31!
01/03/33!
01/10/34!
01/05/36!
01/12/37!
01/07/39!
01/02/41!
01/09/42!
01/04/44!
01/11/45!
01/06/47!
01/01/49!
91
@,91;(&FG6&@1+)',0+-.&;(.-',0+8*,J&4('H((+&'*(&/(+8,'?&0:&(.()';,)&<(*,).(8&-+/&9;,/&;(+(H-.&;([1,;(2(+'8&
Sources: MMEM (2011), E.ON (2010), RWE (2011), RWTH Aachen (2011), Universität Magdeburg (2010)
Except the study undertaken by RWE, which assumes quick charging faci l i t ies in residential areas and hence more exposure of the low-voltage grid to technical ly demanding load flows, al l studies come to the conclusion that below 10 percent market diffusion no improvement of the network are required. In the Market Model, the functional relat ionship forms the base of estimates of future grid investments. In MMEM, grid investment are categorized according to nine regional types cal led BBSR 9er (BBSR is the abbreviation of Bundesinstitut für Bau-, Stadt- und Raumforschung), which can be differentiated according to inner city areas / urban centers (“Agglomeration Kernstadt”, area type no. 1, and “verstädtert Kernstadt”, no. 5), agglomerations (“Agglomeration hochverdichtet”, no. 2, “Agglomeration verdichtet”, no. 3, and “Agglomeration ländlich”, no. 4), semi-urban areas (“verstädtert verdichtet”, no. 6, “verstädtert ländlich”, no. 7), and rural (“ländlich höhere Dichte”, no. 8, and “ländlich geringere Dichte”, no. 9). The association of the electricity supply industry provides data for each area type that can be used to calculate the length of the medium and low voltage grid per person. The following table shows the categorisation and the result ing grid length.
Area type
Surface in
km2 Population
Inhabitants / area
Length grid/ inhabitant
(m)
Distance grid per
area (km)
1 Agglomeration Kernstadt 8 746,79 19137982 2188 8 153104
2 Hochverdichteter Kreis 26 412,98 14065290 533 15 210979
3 Verdichteter Kreis 28 889,32 6235117 216 25 155878
GZ!
#GZ!
:GZ!
;GZ!
<GZ!
=GZ!
>GZ!
?GZ!
@GZ!
CGZ!
#GGZ!
GZ! #GZ! :GZ! ;GZ! <GZ! =GZ! >GZ! ?GZ! @GZ! CGZ! #GGZ!
^()(88-;?&9;,/&;(+(H-.&,+&c&
dC*-;(&0:&(.()';,)&<(*,).(8&,+&'0'-.&<(*,).(&8'0)Le&
M[JB!*'F.2!.'0./! M[JB!/031E*'F.2!.'0./!
M[WH!L.+-02B!A1,-(*,!)03.2)!/1)0!3.2.D0302,! M[WH!L.+-02B!A1,-!)03.2)!/1)0!3.2.D0302,!
J9_TB!'*'.4!D'1)! Y21!^.D)0F*'DB!'070'02+0!D'1)!
T0+0//.'8!D'1)!'020A.4!12!Z!"^^J^$!
92
4 Ländlicher Kreis 28 288,05 2938433 104 32 94030
5 Kernstadt 4 340,39 4888817 1126 8 39111
6 Verdichteter Kreis 74 882,32 15899001 212 15 238485
7 Ländlicher Kreis 78 215,28 8368075 107 25 209202
8 Ländlicher Kreis höherer Dichte 51 964,67 6871570 132 32 219890
9 Ländlicher Kreis geringerer Dichte 55 365,91 3598071 65 54 194296
Total 1.514.974
Table&68:&&
Source: VDN/VDEW (2010)
Under the following assumptions the total investments requirements associated with the diffusion of electric cars can
be quantified:
! According to ABB (2006), cables typically represent 60 to 80 percent of the replacement value of a medium
and low voltage distribution network
! E-Bridge Consulting GmbH Cable (2011) estimates that grid replacement costs about €40 to €50 per meter in
the low voltage grid and €50 to €80 per meter in the medium voltage network. This figure applies to non-
urban areas and are thus about 85 percent of the total vehicle stock in German households. It can be
assumed, that densely populated, urban areas may have slightly lower costs, but without subsidies for on-
street parking and charging the diffusion of electric vehicles will be fairly low in these areas, according to
the MMEM reference scenario. Hence, we use the E-Bridge cost estimates as a conservative estimate also for
inner urban areas.
For the nine regional types, the following investment needs in the reference scenario can be specified.
93
Figure&69:&Grid&investments&due&to&electric&vehicles&according&to&geographic®ional&characteristics&
Source: MMEM (2011)
The graph shows that substantial grid investments start from around 2020 in semi-urban areas and agglomerations.
They reach their peak in the mid-2030s when more than €300 million will be required each month. Of all geographic
types, replacement costs in urban areas are lowest due to the low local penetration of electric vehicles in the
reference scenario.
However, regular replacements of the low and medium voltage grid would have to take place anyway. According to
ABB (2006), a cable can be used for approximately 50 years. For an exemplary network of 1.000 km length this implies
that on average 20 km of cable per year have to be replaced due to age. A comparison between the generic grid
replacement costs and the specific costs related to electric mobility shows that the share of replacement due to
electric vehicle charging is not negligible, but remains fairly reasonable over the simulation period. Until 2030, about
€5 billion would have to be spent. The cumulated MMEM estimate reaches €15 billion in 2040.
0!
50!
100!
150!
200!
250!
300!
350!01/01/11!
01/09/12!
01/05/14!
01/01/16!
01/09/17!
01/05/19!
01/01/21!
01/09/22!
01/05/24!
01/01/26!
01/09/27!
01/05/29!
01/01/31!
01/09/32!
01/05/34!
01/01/36!
01/09/37!
01/05/39!
01/01/41!
01/09/42!
01/05/44!
01/01/46!
01/09/47!
01/05/49!
Investmen
t&Re
quirem
ent&in&€&m
illion&/m
onth&
Urban!centers! Agglomeraaons! SemiEurban!areas! Rural!areas!
94
Figure&70:&Grid&investments&due&to&electric&vehicles&according&to&geographic®ional&characteristics&
Source: MMEM (2011)
Given that technological innovations, decentralized generation and the emergence of a Smart Grid will fundamentally
change the structure of the electricity system, it remains possible that even larger synergies between electric vehicles
and general grid renovation can be achieved.
6.9.5. Charging infrastructure
Pure battery-electric vehicles require an infrastructure that allows for quick charging while being on the road – with a
conventional plug these car holders would have to cope with long waiting times until their batteries would be fully
charged. The lack of a network of quick charging stations results in an important impediment for car owners who
frequently travel longer distances. First quick-charge stations are currently under construction, but high installation
costs and the immature technology to optimise the interaction between battery chemistry and fast charging stations
delay a rapid spread.
In MMEM, the emergence of privately operated fast charging stations is predicted by a net present value calculation. It
is assumed that only pure battery electric vehicles will use these stations, while range extenders and plug-in hybrids
choose conventional fuels while on the road. The charging requirements are calculated according to the MiD data on
daily travel patterns. The construction of quick-charging stations has a positive feedback loop with the synthetic
utility function via the charging network density variable: The higher the density of quick charging stations, the lower
the “penalty” for a pure battery electric vehicle. As soon as the amount of quick charging stations converges to the
number of conventional gas stations in Germany (around 15.000) the disadvantage disappears and pure battery
electric vehicles can compete in this aspect with all other conventional combustion technologies, range extenders and
plug-in hybrids.
In the model it is assumed that technical problems related to charging characteristics are solved within the next few
years. From a business perspective, the successful operation of a fast charging station is only profitable when retail
prices and demand are sufficiently high. The net present value (NPV) method offers an insight how many fast charge
0!
10!
20!
30!
40!
50!
60!
70!
80!
90!
100!01/01/11!
01/08/12!
01/03/14!
01/10/15!
01/05/17!
01/12/18!
01/07/20!
01/02/22!
01/09/23!
01/04/25!
01/11/26!
01/06/28!
01/01/30!
01/08/31!
01/03/33!
01/10/34!
01/05/36!
01/12/37!
01/07/39!
01/02/41!
01/09/42!
01/04/44!
01/11/45!
01/06/47!
01/01/49!
Cumulah
ve&In
vestmen
ts&[€
&billion]&
Regular!replacement!! EVEinduced!replacement!!
95
stations will be profitable for a given amount of pure battery electric vehicles. It takes the discounted sum of all
expenditures, including installation costs and ongoing expenses, as well as the gains from selling the electricity, into
account. The profit margins is assumed to be 4.5 percent, similar to a conventional gas station, but can be modified
by the MMEM user. The formula for the calculation of the NPV consists of the following elements:
3-4.(&G56&W-'-&,+J1'8&:0;&'*(&[1,)L&)*-;9,+9&^DR&)-.)1.-',0+&
Source: MMEM (2011)
The NPV is then computed by:
"#<$!
where
"#=$!
An ind iv idual qu ick charg ing un it i s prof i tab le i f the Net Present Va lue i s pos i t ive. For NPV = 0 fo l lows
"#>$!
With this formula, the critical threshold of pure battery electric vehicles is determined, and the number of profitably
operating quick charging units can be calculated.
In the MMEM reference scenario, the amount of quick charging units increases modestly over the simulation horizon. In
2020, about 200 quick charging stations will be installed, if investment costs are assumed to be !15.000 per unit.
( ) PVFCQnSubCNPV MOxEVInv !"!#!++"= &
FuelFuel CP !="
An ind iv idual qu ick charg ing un it i s prof i tab le i f the Net Present Va lue i s pos i t ive. For
!
Q
SubCPVFC
nMO
Inv
crit !"
#+=
&
96
Figure&72:&Number&of&profitably&operating&quick&charging&units&in&Germany&
Source: MMEM (2011)
However, the number of quick charging units can be substantially increased by adding a state subsidy to compensate
for the high installation costs. In addition, MMEM does not integrate combined business models, like the cross-
subsidisation of a charging station via sales in a conventional gas station or as an additional service to shopping
centers or restaurants, but requires a “stand-alone” profitability. The MMEM estimate of the amount of quick
charging stations hence has to be considered a lower bound.
0
500
1000
1500
2000
1/1/2011 1/1/2016 1/1/2021 1/1/2026 1/1/2031
Num
ber o
f pro
fiabl
y op
erat
ing
quic
k ch
argi
ng u
nits
97
7. Cost Benefit Assessment
7.1. Introduction and General Approach
Following a year-long consultation process with stakeholders from industry, academia, non-governmental
organisations and politics, the German government has agreed on a policy package to promote electric mobility. The
measures consist of tax incentives, subsidies as well as financial support for research and development. Policy makers
and industry stakeholders alike argue that increased public spending, or losses through reduced tax take, will be
offset by the wider societal benefits provided by electric mobility. However, despite the considerable scale of the
funding, a rigorous assessment of the benefits and costs of electric mobility is yet missing. In this section we attempt
to assess the net benefits arising from policies that aim at promoting electric mobility compared to the reference case
without a policy package.
Our approach uses a standard cost benefit assessment. The analysis is comparative in that we evaluate what changes
are caused compared to a reference scenario without policy intervention.
7.2. Discounting rate and time horizon
7.2.1. Setting a discounting rate
The discussion of how much and when to discount future cash flows has been going on for a considerable time. It was
posed, and simultaneously answered, already more than eighty years ago by Ramsey stating that a discounting of
future welfare is not ethical (Ramsey 1928). However, despite these early attempts the debate has not yet settled on
a commonly agreed solution.
The term discounting rate refers usually to the ‘social discounting rate’ that is used to discount monetary cash flows
(in real terms). The rate itself is an aggregation of two components derived from the ‘Optimal Growth Theory’: The
so-called pure time preference rate δ and the welfare-weighted growth rate η g.
ρ= δ + η g
The time preference rate reflects inherent impatience – a payment today is considered more valuable than the same
payment in the future. While this notion is uncomplicated in an intra-generation context discounting future cash flows
of the following generations is controversial and requires an ethical justification. Indeed, assuming a time preference
of 2% would half the value of the next generation (35 years).
Considering, the welfare weighted growth rate as a second component reflects the notion that future generations
benefit from higher growth rates today. A high expected growth rate increases the discounting factor and in turn
reduces the weight that is given to future cash flows in an inter-temporal cost benefit assessment. It is also often
argued that the economic growth rate needs to be welfare weighted. The weighting factor η can be interpreted as a
measure of intergeneration inequality. Indeed, increasing η would disproportionately increase the effect of economic
growth onto the discounting factor. High ηs thus reflect a strong sense for intergeneration equality.
Meanwhile, there are a number of studies that assess the inter-temporal aspects of economic and environmental
scenarios and use social discounting factors in the process. Worthwhile to mention in this contexts are the studies
undertaken by Nordhaus, Cline and Stern (see Table 70).
98
� � Economic growth*
�
Cline 0 1,5 1,5 2,05 Nordhaus 3 1 1,5 4,3 Stern 0,1 1 1,5 1,4 Table&73:&Social&discounting&rate&examples&
Sources: (Cline 1992), (Nordhaus 1994), (Stern 2006). Table based on (Dasgupta 2008)
The overview suggests that differences are mostly due to different views of time preference δ. While Cline and Stern
adhere to the Ramsey view, Nordhaus assumes efficient capital markets and sets his preference rate in line with
interest rates expectations.
In line with the past and current discussion in the context of environment and climate policy MMEM sets the time
preference rate to 0. Thus cash flows of future generations are considered equal to those of current ones.
Regarding the welfare weighted economic growth rate we follow (Dasgupta 2008). Setting η to 2 would suggest in
their reasoning a capital accumulation rate of 32% - a reasonable assumption. Furthermore, a value of 1 would come
with the strong assumption that intergenerational distribution is without any importance. Specifically, we suggest
setting η to the value 2. This in combination of expected trend growth of around 1.5% p.a. over the forecasting
horizon would lead us to a social discounting rate of 3% p.a.
The time horizon of the cost benefit assessment is 2012-2050. However, shorter time horizons are calculated and will
be reported for illustrative purposes and to monitor welfare changes over time.
The cost benefit analysis in MMEM is restricted to the national boundaries and considers only impacts affecting
stakeholders based in Germany. So while we calculate some impacts of the ‘rest of the world’ actors – such as
producer rent of fossil fuel producers – for reporting reasons, the final cost benefit balance does only reflect changes
within the national boundaries.
Finally, the cost benefit analysis is strictly comparative. Any assessment of effects is in comparison to the reference
case (‘no policy scenario’). The reference case reflects our view in a business as usual world. Specifically, it only
considers policy measures that have been implemented already – for example the road tax exemption period for
electric vehicles – and those that have been announced and agreed upon as per spring 2011.
7.3. Policy Scenario
The German government wants the country to become a global lead provider and market for electric mobility. As a
milestone to achieve this objective a target of 1 Million electric vehicles on German roads by 2020 has been
announced. As the reference scenario suggests there is a chance that the target could be missed without policy
support.
However, the support measures considered, and partly agreed upon already, require substantial government funding.
As such, it would be insightful to investigate if the societal benefits arising from a scenario where 1 Million electric
vehicles will be in the vehicle stock by 2020 warrants these additional expenditures. Consequently, we have simulated
a scenario that will see the number of electric vehicles purchases increase compared to the reference scenario and
lead to a stock of around 1 Million electric cars by 2020. In the first instance we will pay little attention to the actual
policy mix that is employed to reach this target but concentrate on the cost and benefits compared to the no policy
reference scenario.
99
For illustration purposes the only policy instrument employed is a purchase subsidy paid towards car buyers when they
purchase electric vehicles. Our analysis suggests that lump sum purchase incentive of €3000 for the first 500,000
buyers of electric vehicles would increase consumer acceptance to a level that would see around 1 million electric
vehicles in the vehicle stock by 2020. 1
&
Figure&74&I&Electric&vehicle&stock&policy&and&reference&scenario&
Source: MMEM (2011)
7.4. Impacts considered
The MMEM cost benefit framework is comprehensive as it considers all major stakeholder groups and all significant
impacts that are likely to arise from policy measures affecting the passenger transport sector. We calculate and
report impacts by stakeholder groups. As such, any policy measure can be assessed in detail in respect to its effect on
different parts of society. However, while we separate the impacts by stakeholder groups we do not apply any
weighting factor for selected groups. Accordingly, a policy measure is only considered welfare increasing when it
leads to an overall net benefit to society – that is, at least one stakeholder group needs to gain a higher net benefit
than the rest of society loses. A sole distribution of benefits cannot lead to an overall welfare increase and would lead
to a net benefit of zero. The main impacts estimated within MMEM are summarised in the table below.
1!! A!purchase!incentive!is!one!of!many!possible!policy!instruments!available.!Tax!reductions,!fuel!tax!changes,!a!bonus! malus! road! tax! system! are! other! instrument! that! would! affect! demand.! On! the! supply! side! research! and!development!subsidies!or!emission!standards!are!further!examples.!!
0!
2!
4!
6!
8!
10!
12!
14!
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100
Impact areas Consumers Producers Government
Environment
Rest of the World
Car purchase Change in consumer welfare car purchase expenditure
Producer rent car sales
Direct policy funding costs
Foreign producer surplus car purchase
Fuel consumption
Fuel expenditure welfare
Producer rent fuel production
Fuel tax revenues
CO2 damage costs
Foreign fuel production and sale producer surplus
Producer rent electricity production
Energy tax receipts
Other pollutants damage costs
CO2 abatement costs
Fine payments
Road tax Road tax l iabi l i ty
Road tax receipts
Infrastructure
Home charging infrastructure
Home charging infrastructure
Grid extension
Grid extension
Policy costs Shadow costs public expenditure
Shadow costs public expenditure
VAT receipts
Table&75:&Overview&impacts&considered&
Source: MMEM (2011)
7.5. Consumers net benefits in the policy scenario
7.5.1. Changes in car purchase expenditure of the policy
Electric vehicles are characterized through substantially higher investment costs compared with conventional
vehicles. This is largely driven by the higher costs of batteries. A policy that aims at increasing the diffusion of
electric cars is likely to have a significant impact on the level of car purchase consumption expenditure.
This section assesses the policy impact on car purchase expenditure. We will consider how the policy affects overall
spending on car purchases over the policy horizon in comparison to the base scenario. In addition to changing the
actual investment costs for buyers, the policy is also likely to impact consumer welfare as consumption levels change
101
compared to the base scenario. That is, by increasing or decreasing purchase costs for vehicles a policy may lead to a
net increase in or reduction in the number of cars bought. Thus, our approach also estimates the impact on consumer
welfare arising from the policy measure.
As a policy measure will affect car purchase spending over several periods – a characteristic that is inherently
considered in the dynamic nature of the EMOB modeling approach – we need to compare expenditure over the entire
policy horizon 2012-2050. Indeed, a policy that promotes the diffusion of electric vehicles may indirectly impact
future car purchase expenditure as economies of scale and learning effects can have a lasting effect on the cost and,
in turn, the purchase price and car purchase expenditure.
The following equation shows our approach to calculating cumulated discounted car purchase expenditure over the
policy horizon:
Equation 1 - Cumulated discounted car purchase expenditure formulae
! ! "#,%&'#,% &(#,%Π%#=1,%%=1
!
With p!,!! denoting the average price of a typical car in each segment technology combination i , the capitalised P!,! denotes the purchase probability, or market share, for a consumer to buy a car in the segment i. Finally, q!,! represents aggregated car demand – that is, the total units of cars bought in period t. Thus, aggregating car purchase
expenditure over all 99 technology segments combinations i provides an estimate for aggregated car purchase
expenditure in period t. Future expenditure is discounted using the social discounting rate Π!.
Following this description of the basic set up, we move now on to the actual simulation results for the policy impact.
The chart below shows the top level impact of the policy package on car purchase expenditure.
Figure&76:&I&Comparison&of&discounted&car&purchase&expenditure&
Source: MMEM 2011.
20!€!
30!€!
40!€!
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60!€!
70!€!
80!€!
2011!
2012!
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2015!
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2017!
2018!
2019!
2020!
2021!
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s&
Policy!E!Car!purchase!expenditure!(discounted)!
Ref!E!Car!purchase!expenditure!(discounted)!
102
Comparing the two scenarios (policy case and reference scenario) shows that the policy measure, although temporary,
has a lasting impact on car purchase expenditure. Initially, before 2020, it is likely to increase expenditure on car
purchases reflecting the increase in demand from the subsidy. This effect is likely to reverse when the policy support
expires.
Secondly, on the whole, the incentive leads to a decrease in car purchase expenditure compared to the reference
case (without the policy package). Indeed, when comparing cumulative car purchase expenditure (discounted)
between 2011 and 2050 we find it has decreased by a total of €42,990 Mio – a reduction of 2.5% compared to the
reference scenario.
While overall expenditure decreasing over the policy horizon, determining the overall net benefits to society should
be based on a welfare approach. Specifically, we can distinguish two effects from the policy measure: the price and
the quantity effect. On the one hand the purchase incentive reduces the market price of cars and in turn car purchase
expenditures for those who already would have bought vehicles in the reference scenario (price effect). Additionally,
the measure makes cars more affordable and, in turn, leads to an increase in the overall quantity of cars bought. This
combined price and quantity effect lead to a welfare change that can be attributed to the policy measure.
We use the so-called ‘Rule of One Half’ as a linear approximation of the welfare change for consumers resulting from
both effects.
Equation&1&I&'Rule&of&Half'&formula&
∆"#$%&'()*+&),-&% = 12 (21 + 20)(,0 − ,1)!
We find that the policy package leads to an overal l increase in consumer welfare (see Figure 77). The combined impact of lower investment costs for exist ing car buyers and increased affordabil i ty for new buyers can be valued at around €4,900 mil l ion over the policy horizon. As such, the policy measure has had a significant net benefit to consumers by reducing the purchase costs of new vehicles.
Figure&77:&Discounted&consumer&welfare&change&from&car&purchase&expenditure&
Source: MMEM 2011
Somewhat surprising though, is the lasting nature of the welfare increase following the end of the subsidy in 2019
when the cap of 500,000 vehicles is reached. A closer examination of the results shows that much of the benefits for
0!50!
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consumers arise from a reduction in the purchase costs of conventional vehicles – such as diesel and gasoline cars. As a
result of the increased share of electric vehicles car manufacturers find it easier to meet emissions standards set by
the EU 443 regulation. As the standards are compared to average emissions any low emission electric vehicles that
substitutes a conventional car reduces the fleet-weighted emission average. Consequently, with the subsidy increasing
the number of low emission electric cars, manufacturers have an incentive to relax emission-reducing but cost
intensive improvements of conventional combustion engines (for example a start-stop automatic). This reduces the
cost of vehicles and is reflected in higher consumer welfare in respect to car purchase expenditure.
7.5.2. Fuel costs expenditure
Savings in fuel expenditure are one of the key arguments in favour of an increased use of electric vehicles. Indeed,
electric vehicles operate with an efficiency of up to 95% while internal combustion engines usually only reach around
40% energy efficiency (McKinsey&Company 2009). At current energy prices this leads to a fuel cost advantage of
around 45% for pure battery electric vehicles for each unit of energy used for propulsion (hybrid concepts will have a
smaller cost advantage depending on the actual share of overall vehicle performance derived from the electric
engine).
In order to assess the effect of the policy package on fuel expenditure, we follow a similar welfare approach as in
section 7.5.1 where we estimated how the policies would affect car purchase expenditure.
Aggregate fuel expenditure of an economy depends on the number of cars, the annual distance driven and the fuel
costs per kilometre. Fuel costs, in turn, depend on the fuel efficiency of the propulsion technology expressed in units
of energy used for each kilometre distance of propulsion and the price of each unit of energy.
Equation 2 shows how we calculate fuel expenditure for a specific vehicle segment for a given time period. Total
national fuel consumption is then the fuel expenditure aggregated over all vehicle segments.
Equation&2&I&Fuel&expenditure&for&a&specific&segment&in&period&t&
!"#$% ,'( = *'( +,(' +-'( +.'( !
Where y!"#$,!" is the fuel expenditure of all vehicles in segment ij in time period t (the time index has been omitted for
simplicity purposes), Q!" denotes the stock of registered vehicles in each segment technology combination ij in period
t, x!" is the average distance travelled by car users in segment ij over period t, and c!" denotes the average fuel price
expressed in Euros per unit fuel. Finally, !δ!" donates the actual fuel consumption (units of fuel per kilometre driven)
of a typical vehicle in each segment technology combination in the vehicle stock.
A government policy can impact fuel expenditure in several ways. Firstly, it can change the mix of propulsion
technologies in the vehicle stock (Q!") as consumers change their buying decisions for a different type or car segment.
Secondly, a policy can also change the fuel efficiency !δ!" of internal combustion engines as car manufacturers react to
any change of the market shares with an increased or less ambitious improvement of combustion engines. This, in
turn, affects how much fuel vehicles consume. Within MMEM we have made the simplifying assumption that the annual
distances driven stay constant over time and are not affected by any policy measure. Table 78 shows the assumed
annual driving distance based on the Mobilität in Deutschland survey (MiD 2008).
&
104
Segment
Weighted annual driving distance [km/yr]
Minis 10500 Kleinwagen 11400 Kompaktklasse 13900 Mittelklasse 16000 ObereMittelklasse 15700 Oberklasse 13500 Geländewagen 14800 Sportwagen 10200 MiniVans 14300 GrossraumVans 17100 Uti l i t ies 15700
Table 78: Weighted average annual driving distances
Source: (MiD 2008)
As a government policy can affect both, the quantity of fuel consumed as well as the price of fuel we use the ‘Rule of
One Half’ approach displayed above to approximate the welfare effect on consumers.
Figure 79 shows the simulation results for consumer welfare caused by the policy scenario in question. It suggests that
the policy package would lead to a substantial decline in consumer welfare. On balance, after discounting, we find
that the policy measure would lead to a net cost through reduced consumer welfare of around €5,238 million.
Surprisingly, the main impact is being felt after 2020 and thus after the actual policy measure has ended.
The explanation for the negative impact is based on the same effect that led to a positive impact in the context of car
purchase expenditure. With the policy leading to a higher share of electric vehicles car manufacturers find it easier to
meet emission standards (that are based on average fleet weighted emissions thresholds). As a consequence they
reduce their efforts to improve conventional engines which leads to more high emission (and fuel consuming) vehicles
entering the market. So while consumers save money as they buy cars they need to spend more on fuel as, on
average, the cars offered are now less fuel efficient as in the reference scenario.
105
Figure&79:&Fuel&consumption&expenditure&
Source: MMEM 2011
In conclusion, consumer welfare is affected by two effects: Increased welfare as improvement cost decrease and
hence cars become on average more affordable. Secondly, reduced welfare from rising fuel costs as less efficient
vehicles enter the market. Overall, there is indication that the net effect is negative. In any case, a policy that aimed
at reducing fuel expenditure has led to the opposite effect of less efficient passenger cars.
7.6. Infrastructure costs
In this section we consider changes to infrastructure spending, in particular, charging infrastructure and grid
extensions that have been triggered by the policy measure. Such infrastructure expenditure is likely to affect
consumers – who need to buy charging equipment or are likely to pay for grid investments via higher electricity prices
– as well as producers that produce, install and maintain infrastructure and may earn producer rent in the process.
7.6.1. Home charging infrastructure
Battery electric vehicles – and to some degree Plug-in Hybrid cars and Range Extenders – need to recharge their
battery on a regular basis. This requires a suitable charging infrastructure. In turn, a policy that affects the number of
electric vehicles will have an impact on how much money needs to be invested in charging devices as the number of
charging points required has changed.
The actual investment costs for home charging devices depend largely upon which parking facilities are available to
the car user. Car owners that own or rent private parking space with access to the energy grid require little additional
investment and operational expenditure to recharge their vehicles. Indeed, in the simplest case the electric car could
be recharged using a normal domestic socket outlet. In this case it seems reasonable to assume that charging
infrastructure spending is contained to a simple high voltage socket including a fee for installations estimated as a
one-off expenditure of around €110.
A large scale survey of car owners carried out in Germany in 2008 (MiD 2008) suggests that around 70% of car owners
do have their own parking space available. We assume that this share is also representative for buyers of new cars.
E450!€!
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The situation fundamentally differs though, for those 30% of car owner who do not have access to off-street parking
and normally park their vehicles in public spaces. Indeed, potential electric car users without a private parking space
would face considerable costs in order to be in the same position as those with a private garage or other forms of off-
street parking. They would have to hire a private parking space with access to the electricity grid. For our analysis,
we distinguish between those two user groups when analysing home charging investment cost.
Our research (sumarised in Table 80Error! Reference source not found.) indicates annual parking hire costs in the
range of €517-766€ per year depending on the type of region (with parking hire costs generally higher in urban areas).
Additionally, suitable charging equipment would need to be obtained. We would expect the costs for charging
equipment to lie in the range of €2,400 to €4,600 depending on the region2. We also factor in exogenous equipment
cost reductions due to scale economies and learning by doing effects until 2020 (see section Error! Reference source not found. for detailed review of equipment costs).
&
User type
Annual equipment hire costs
Annual parking hire costs
Total annual expenditure
2011 630 € 550 € 1,180 € 2020 430 € 550 € 990 € Table&80:&Assumptions&of&equipment&costs&for&users&without&private&parking&
Source: (MiD 2008)
Overall, looking at the annual costs, our research leads to the assumption of total expenditures of around €1,200 per
year in 2011 which could decrease to around €1,000 per annum in 2020 for electric vehicle buyers without own
parking. These significant extra costs for potential electric vehicles users without a private parking space explain that
there are considerable differences in the market penetration between the two user groups as the 30% of buyers
without private parking facilities would factor in these additional annual costs.
Looking at the simulation results, we observe that off-street charging investment costs grow exponentially in line with
the increasing electric vehicle market penetration. The chart below shows that infrastructure spending for off-street
charging devices is likely to reach around €800 Million per year when full market penetration has been reached.
2!We!assume!an!asset!life!time!of!10!years!to!derive!annual!equipment!costs!estimates.!
107
Figure&81:&Aggregated&spending&on&offIstreet&charging&devices&
Source: MMEM 2011
The chart also shows that the policy package has had a significant impact on demand and spending for off-street
charging devices. Furthermore, this impact is not only limited to the time when the policy is effective but also – as
the stock of electric vehicles remains higher than in the reference scenario – continues to incur higher cost for
consumers well into the future.
We find that the policy impact has triggered additional expenditure of €1,054 million. After discounting future
payment flows, the net present value of the additional costs amounts to €664 million.
While consumers are faced with higher expenditure, producers are likely to benefit through increased revenues and
producer rent. We estimate that producer rent from charging equipment manufacturing, installation and operation &
maintenance activities increases by €99 million over the period in question (see section 7.8. for detailed assessment
of producer rent impacts and the underlying assumptions).
7.6.2. Grid investments
Besides an additional charging infrastructure for individual car owners increased use of electric vehicles may also
require electricity grid upgrades. The underlying assumptions are explained in section Error! Reference source not found. Error! Reference source not found..
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108
Figure 82 Grid investment spending
Source: MMEM 2011
We find that a policy scenario that promotes electric vehicle use and would require more and earlier investments into
the electricity grid. Compared to the reference scenario, additional discounted costs of around !541 Million will be
incurred between 2012 and 2050. While these investments would be initially made by grid operators, it is likely that
the costs will be transferred to consumers via higher electricity bills. We therefore consider this to be a net cost to
consumers.
For producers, on the other hand, increasing grid investments are likely to lead to net benefits in the form of increase
producer rent. Based on gross profitability3 observed in the industry in past we would expect the additional
investment spending to cumulate to a net benefit of !51 million.
7.7. Government finances
As a third stakeholder the government is the initiator of the policy measures and has to bear the corresponding policy
costs. It will also be impacted as changes in the behavior of the economic actors affect its tax receipts and
expenditure in several areas.
Specifically, we have identified the following fiscal impact areas which are likely to be affected by policy measures to
promote electric mobility:
• Direct policy costs (e.g. the cost of subsidising electric car purchases),
• Energy tax receipts on automotive fuels as well as electricity,
• Road tax receipts,
;!! J*'(/,.,!/,'*+,*'.4!F*/120//!12)1+.,('/!"TLSJ!M#B!K2/,.44.,1(2!(7!040+,'1+.4!A1'12D!.2)!71,,12D/$!/*DD0/,!.!D'(//!&'(71,!3.'D12!(7!C9=Z!F0,A002!#CC@E:GG?!"'070'!,(!/0+,1(2!?9@!7('!)0,.14/$9!!
109
• Value added tax revenues from car purchase expenditure, fuel sales, electricity consumption and investments in
charging infrastructure equipment,
• Fine payments in respect to EU Regulation 443.
The impacts and assumptions made to estimate them will be explored in the following section of the report.
7.7.1. Direct policy costs
Most of the policy measures considered will have some direct costs attached to it. This is clear in the context direct
subsidies like the purchase incentive discussed here. But is also the case, although less straight forward to estimate,
for tax reduction schemes like some of the tax breaks and depreciation allowances for corporate buyers of electric
vehicles that have been discussed. These costs need to be estimated and factored into any cost-benefit assessment of
policies to support electric mobility. That way the claim that the cost of investing in electric mobility will be
outweighed by its benefits can be investigated in earnest.
To recall, in this particular case we consider the effects of a purchase incentive for buyers of electric vehicles.
Specifically, each buyer receives a one of subsidy of €3000 per vehicle stating in 2012. The total number of purchases
that will be subsidized is capped at 500,000 vehicles. Consequently, the nominal policy costs are €1,540 million – with
a not unrealistic cost overshoot as the policy cannot be ended instantly but only on a monthly basis. After discounting
the estimated net cost to the government are €1,258 million. As Figure 83 suggest the main burden for the public
purse will be concentrated towards the end of the policy horizon as the market penetration of electric vehicles
gathers speed.
Figure&83&Direct&policy&costs&
Source: MMEM (2011)
7.7.2. Energy tax revenues
Energy tax comprises tax receipts from fuel excise duty as well as energy tax on electricity sales. An increasing share
of distances driven with electric vehicles would consequently increase revenues from energy tax on electricity while
reducing fuel excise duty revenues. With an estimated tax take of €27.4 billion from passenger vehicles in 2011,
already small changes would have a significant impact on net benefits of the government and eventually on the actual
cost benefit balance of the policy measure.
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There are two points worth making in respect to energy tax changes arising from electric mobility. Firstly, electric
engines are inherently more fuel efficient than internal combustion ones. Thus, for each kilometer driven less energy
is consumed reducing in turn the tax income. This is further aggravated by the fact that each unit energy combustible
fuel has a four-fold higher tax rate than electricity. Indeed, while gasoline is currently taxed at around !0.07-0.08 per
kWh electricity energy tax is only around !0.02 per kWh.
Indeed, the comparing fuel tax revenues between the policy and reference scenario shows a significant reduction over
the policy horizon. The cumulated discounted net cost is estimated to be !2.35 billion. While large in absolute
numbers this is only 0.6% compared to the reference scenario. A closer examination shows that the effect has been
mitigated considerably by the reduced fuel efficiency of the conventional vehicles as manufacturers have less
incentive to optimize vehicles.
&
@,91;(&KB&T*-+9(&,+&/,8)01+'(/&:1(.&'-7&;(<(+1(8&
Source: MMEM (2011)
So there are two opposing effects. Firstly, an increase in electric mobility reduces combustible fuel consumption and
hence revenues from fuel excise duty. Secondly, the increased fuel consumption of combustion engines (through the
aforementioned incentive to reduce improvement efforts) resulted in an increase of fuel excise duty. On the whole,
we find that the former effect outweighs the opposing effect from less efficient cars leading to a fuel tax loss for the
government.
However, the government is likely to benefit from higher tax take on energy tax on electricity. However, this increase
cannot compensate the entire fuel excise duty loss since the tax rate on electricity is significantly lower than the one
on combustible fuels. Figure 85 shows the change in energy tax receipts (discounted) arising from the policy measure
which we estimate to cumulate to a net benefit for the government of !256 million – significantly lower than the tax
lost in fuel excise duty.
111
Figure&85:&Discounted&change&in&energy&tax&receipts&
Source: MMEM (2001)
7.7.3. VAT tax revenues
Another major fiscal impact for the government is likely to arise from changes in value added tax revenues. We have
analyzed several areas in which expect the main impact to occur. The largest impact can be expected from VAT on
conventional fuel, which is expected to fall in line with fuel excise duty.
This will only be partly compensated by an increase in tax revenues on electricity sales (up €392 million) and VAT on
home charging equipment expenditure which is expected to increase by €126 million.
Overall we expect net costs of €1,186 million for the government over the forecasting horizon (Table 86)
&
VAT receipts (discounted) in € million
VAT electricity sales 392
VAT car sales -367
VAT ICE fuel spending -1,210
Total NPV VAT Home
charging devices
126
Total NPV VAT -1,186
Table&86:&Discounted&change&in&Value&Added&Tax&revenues&
Source: MMEM (2011)
7.7.4. Road tax income
Road tax forms another pillar of Germany’s taxation system of transport. Following some changes in the last year,
road tax has now two components which determine the overall annual tax rate for a vehicle.
One determinant is based on the engine size of the car. However, the tax rate is further distinguished by the fuel type
with diesel powered car liable for a considerably higher rate for an equal engine size than gasoline cars.
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A second determinant is the CO2 emissions of the vehicle. The actual emission factor – measured in g/km – of each
vehicle is compared to a threshold value. Vehicles that are above this threshold apply for higher road tax rates. The
threshold value will fall more or less in line with the standards EU 443 CAFE regulation thus accounting and
incentivising for future efficiency improvements of cars.
&
Figure&87&Change&in&discounted&road&tax&payments&
Source: MMEM (2011)
As is indicated in Figure 87 we expect an increase in road tax payments in the policy which will cumulate to a net
benefit of around €1.17 billion for the government4. This is a 0.7 per cent increase compared to the tax revenues in
the reference scenario – by no means a large shift. Despite electric vehicles being favorably treated by the current
road tax system (due to their low emission levels), increased fuel consumption and hence emissions for conventional
vehicles leads to this overall increase in road tax revenues.
Please note that tax revenues for the government are exactly reflecting tax liabilities for consumers. Therefore any
change in energy, VAT and road tax payments will turn up with the opposite sign in the cost benefit balance of
consumers.
7.7.5. Opportunity costs of public funding
The Opportunity Cost of Public Funds (OCPF) can be considered as a measure of the “real” cost of taxation. Because
taxation is not neutral for the economy, once the Public Sector decides to collect funds the situation of economic
agents is affected and the society as a whole suffers a loss of efficiency. In other words, raising one unit of public
funds costs more than one to the society.
However, the notion of opportunity cost of public funding is not well defined and (Massiani 2011) finds that it
generally represents at least three phenomena: micro economic deadweight loss due to taxation, administrative costs
(and corruption), and the crowding out of private investment. However, the author finds that all these elements have
4!A!corresponding!net!cost!for!consumers!for!which!this!is!a!liability.!!
!(50)!
!E!!
!50!!
!100!!
!150!!
!200!!
!250!!
1! 3! 5! 7! 9! 11!
13!
15!
17!
19!
21!
23!
25!
27!
29!
31!
33!
35!
37!
39!
Millions&
Series1!
113
their place in economic theory and favour an holistic interpretation of the concept of opportunity cost which would
appropriately encompass these different mechanisms.
Based on a literature review (Massiani 2011) suggests a provisional opportunity costs of public funding the range of
0.2-0.3 for western countries, thus assuming that every euro used by public authorities has an actual cost of 1.20-1.30
euro. Following this line of argumentation we set the opportunity costs at the upper end of 0.3 reflecting high
marginal tax rates in Germany. As such, any change in the net benefits of the government has a multiplier of -0.3.
This effect can go in two directions causing a net benefit for the rest of society when the government has a net cost -
ie, the public funding burden is decreasing. Opportunity costs increase if the government has net benefits from a
policy measure, that is the burden of taxation is increasing and in turn the shadow costs of public funding.
In our example, the policy measure – through its direct and indirect impact on public finances – results in €3.7 billion
net costs to the government. That would mean a reduction of opportunity costs of public funding of around €1.1
billion for consumers and producers. That leaves the question of how to attribute these benefits to the two
stakeholder groups. We argue that the share of economic activity is a suitable allocation key in the absence of any
more elaborate measures. Based on this reasoning and national account data we attribute 37% (€411 million) of the
opportunity costs reduction to the households and 63% (€692 million) to producers.
7.8. Industry
This part of the report analyses the impact on producer rent including car production and trade, fuel sales and energy
production.
In order to quantify the impact on producers we estimated the change in economic activity in the areas most likely to
be affected by the policy measures. Specifically we estimated:
• Producer rent in car manufacturing and trade
• Producer rent from combustible fuel production and distribution
• Producer rent electricity production and distribution
• Manufacturing, installation, operation and maintenance of charging infrastructure
• Producer rent from grid extension investments.
We use gross profits as a measure for changes in the producer rent. The underlying profit margins are derived from
Eurostat (Eurostat 2011).
&
114
Turnover or g ross premiums wr i t ten
Gross opera t ing surp lus
Gross opera t ing surp lus/turnover (g ross opera t ing ra te )
Sec tor Mio . EUR Mio . EUR %
Sa le o f motor veh ic les 135 ,970 12 ,954 9 .5
Const ruc t ion 143 ,848 12 ,559 8 .7 Product ion and d i s t r ibut ion of e lec t r ic i ty 225 ,769 21 ,342 9 .5 Reta i l sa le o f au tomot ive fue l 11 ,901 1 ,271 10 .7 Ins ta l l a t ion of e lec t r ica l w i r ing and f i t t ings 16 ,024 1 ,705 10 .6 Manufac ture of e lec t r ic i ty d i s t r ibut ion and contro l appara tus 52 ,153 2 ,703 5 .2 Manufac ture of e lec t r ic domest ic app l i ances 12 ,043 494 4 .1 Product ion of au tomot ive fue l s 134 ,361 765 0 .6 Repa i r o f househo ld app l i ances (O&M of charg ing in f ras t ruc ture ) 328 76 23 .3 Table&88&Assumed&profitability&relevant&industries&(2007)&
Source: (Eurostat 2011)
Based on the assumed profitability and change in economic output we expect the largest impact to occur in the area
of fuel production and distribution as a higher share of electric vehicles leads to a reduction in demand for
combustible automotive fuels (despite this effect being mitigated to a large extend by increased fuel consumption of
the vehicle fleet as car manufacturers relax their improvement efforts).
Car manufacturers are, despite the significant changes affecting the industry only partly affected. In this particular
case we would expect a slight increase in producer rent due to increased demand from the policy stimulus. However,
this will be offset to a large degree by reduced value added and hence profits in the area of engine efficiency
measures. Indeed, one of the key findings of our analysis is that both producers and consumers could be better off
through tighter emission standards. Consumers would overcome their myopic car choice and being forced to buy more
efficient (but pricey) cars but saving fuel costs over the usage time. Manufacturers, on the other hand, would increase
their producer rent from more expensive cars as the additional efficiency measures increase their value content and
in turn their profits.
Other positive effects on producer are likely to occur in the area of electricity production and distribution and
investment in charging infrastructure and grid extension. Table 89 below presents the estimated change in discounted
producer rent in the various areas.
&
115
Sector Producer rent
(€ million)
Car manufacturing 47
Production and trade of automotive fuels -363
Production and distribution of electricity 281
Grid investments 61
Charging infrastructure production, set up and O&M 51
Table&89:&Net&benefits&from&changes&in&producer&rent&
Source: MMEM (2011)
7.9. Environmental impact
Potential reductions in greenhouse gas emission are among the arguments most frequently made by proponents of
electric mobility. Specifically, the reasoning goes, due to their increased energy efficiency and lower emission levels,
electric cars could contribute substantially to the ‘de-carbonisation’ of the transport sector.
In this section we estimate changes of emissions resulting from increased electric mobility use triggered by the policy.
Following the estimation of the quantitative impact on emission levels we will assess the connected damage costs and
resulting net benefits – or indeed costs - to society
7.9.1. CO2 emissions
Propulsion of vehicles requires energy which triggers emissions. While the types of emissions produced are manifold
we will focus in this section on CO2 emissions as one of the main driver of climate change. We estimate CO2 emissions
resulting from burning fuel in internal combustion engines or CO2 related to producing the energy used to charge
batteries of electric vehicles through the electricity grid.
The actual amount of CO2 emitted by a certain kind of car depends on a number of factors such as the type of fuel
and propulsion technology, the car size and weight, the age of the car and so on. We will use average emission factors
that distinguish between a vehicle propulsion technology and the car segment size. Given the setup of the MMEM
simulation model we thus obtain 99 different emission factors.
The emission factors change over time as we consider technical changes resulting from car makers efforts to improve
combustion engines. Also, emission factors of electric vehicles are affected by improvements in energy density and
the resulting increase in electric drive shares (see section Error! Reference source not found.). Finally, the emission
factors need to reflect the actual composition of the vehicle fleet in terms of age of the vehicles and technology and
car size segment mix. Indeed, the recent improvements of emission standards of newly registered cars will only
gradually reduce fleet emissions as newly bought vehicles substitute old cars that fall out of the vehicle fleet (Figure
90).
The chart also underlines that average emissions of newly registered cars are expected to fall further as car
manufacturers seek to adhere to European Union emission standards (specifically EU Regulation 443) and the share of
low emission vehicles rises.
116
&
Figure&90:&Average&CO2&emission&factors&(fleet&average&and&newly®istered&vehicles),&g/km&
Source: MMEM 2011
Aggregated emissions from passenger cars depend on the size of the vehicle stock, the distances driven as well as the
emissions per kilometer driven (ie, emission factors). Consequently we calculate emissions for each time period as
follows:
Equation&3&I&Aggregated&fleet&emissions&
!" =$%&'(& ')&&=1
!
With Q! denoting the number of registered cars in each segment i, d! the average distance driven in the respective
vehicle segment, and e! . Consequently, a policy can impact aggregate emission through a number of ways. It can
affect the purchase decision and in turn the composition of the vehicle fleet. Additionally, a policy can affect
emission factors as car makers find it easier or more difficult to meet emission targets (see section Error! Reference source not found.).
In& this& specific& policy& case&we&observe& to&opposing&effects.& The& increased& share&of& electric& vehicles& reduces&CO2I
emissions&from&combustible&automotive&fuels.&However,&the&increased&share&makes&it&also&easier&for&manufacturers&
to& reach& targets& thus& reducing& efforts& to& improve& engine& efficiency.& This& effect& results& in& an& increase& of& average&
emission&factors&of&vehicles&compared&to&the&reference&scenario&without&policy&in&the&short&term.&However,&in&the&
medium&and&long&term&the&increased&share&of&electric&vehicles&leads&to&a&reduction&in&CO2Iemissions.&&
&
Figure 91 shows the relative change of emission factors triggered by the policy in question.
0!
50!
100!
150!
200!
250!
2011!2013!2015!2017!2019!2021!2023!2025!2027!2029!2031!2033!2035!2037!2039!2041!2043!2045!2047!2049!
Policy!average!emissions!new!registraaons!
Fleet!emission!factors!
117
&
&
Figure&91:&Relative&change&fleet&emission&factors&
Source: MMEM (2001)
We estimate that the policy will lead to total reduction of CO2-emissions of around 13 million tonnes between 2012-
2050 – a reduction of 0.6% compared to total emissions in the no-policy case. Using the damage costs estimate of the
German Environmental Protection Agency (Umweltbundesamt) of €70 per tonne CO2 we estimate a total net benefit to
society of €727 million.
7.9.2. Abatement costs
An increase of electric vehicles is likely to increase CO2-emissions arising from the production of electricity. While
(assuming a working emission trading scheme) this will not lead to any increase of absolute emission levels and hence
environmental damage costs, there is likely to be an impact through changing abatement costs. That is, as demand for
electricity increases so do CO2-emissions and in turn the need for electricity producers to obtain certificates or save
emissions in other areas. Either way increasing emission levels will lead to abatement costs. Valuating these can be
based on the market price for emission certificates. However, the actual impact depends significantly on the
assumptions made regarding future certificate prices. Following a frequent line of argument made in the literature we
assume that CO2-certificate prices are going to convert with the actual environmental damage cost in the long term.
Hence, we assume a gradual increase towards the €70/tonne estimate put forward by the German EPA (UBA 2011)5.
As Figure 92 shows, we expect a significant increase in the amount of emissions resulting from increased electricity
demand. The cumulative increase is estimated to amount to 14,288 million tonnes. Valuating this increase using the
assumed development of CO2-certificate prices and after discounting would suggest a net cost of €328 million. We
5!! We!have!made!the!simplifying!assumption!that!the!additional!demand! is!not! leading!to!an! increase! in!CO2!prices!and!thus!to!a!change!in!demand!from!other!sectors.!!
E0.50%!
E0.40%!
E0.30%!
E0.20%!
E0.10%!
0.00%!
0.10%!
0.20%!
1! 3! 5! 7! 9! 11! 13! 15! 17! 19! 21! 23! 25! 27! 29! 31! 33! 35! 37!
Series1!
118
attribute the costs to producers – however, it is not unlikely that these will be passed on to consumers through higher
electricity prices.
Figure&92:&CO2&Emissions&from&EV&grid&charging,&tonnes&
Source: MMEM (2011)
7.9.3. Other tailpipe-pollutants
Apart from CO2-emissions a policy that aims at increasing the share of electric vehicles in the market is also likely to
affect other tailpipe emissions such as carbon monoxides (CO), hydrocarbons (HC), particle matters (PM), and nitrogen
oxides (NOx). Thus, the change in local and global environmental damages caused triggered by a policy measure
should be evaluated and accounted for in the cost benefit assessment.
As a basis for such an evaluation we use the damage cost guidelines of the Umweltbundesamt (The German
Environmental Protection Agency) outlined in Table 93: Damage cost estimates other pollutants.
&
CO HC NOx PM
€/kg 0.027 0.625 10.6 64.8
Table&93:&Damage&cost&estimates&other&pollutants&
Source: (UBA 2011)
The following initial emission factors are assumed for 2011 with emission factors changed in line with CO2 emission
factors for the following years.
&
0!
1!
2!
3!
4!
5!
6!
7!
8!
9!
10!
1! 3! 5! 7! 9! 11!
13!
15!
17!
19!
21!
23!
25!
27!
29!
31!
33!
35!
37!
39!
Millions&
Series3! Series1!
119
g/km CO HC NOx PM
Diesel 0.182 0.043 0.497 0.036
Gasoline 1.190 0.035 0.103 0.000
Hybrid 1.169 0.034 0.101 0.000
LPG/CNG 0.735 0.012 0.047 0.000
Table&94:&Other&pollutants&emission&factors
Source: (UBA 2011)
Based on the assumptions made above, we estimate that the policy measure will lead to a combined reduction in
damage costs (discounted) from other pollutants of €266 million.
7.10. Overall assessment and conclusions
Error! Reference source not found.On balance we expect this policy measure to result in net costs of €4.244 billion
to society if all effects between 2012 and 2050 are considered. Looking at the specific stakeholder groups only
producers (€440 million] and the environment (€954 million) are likely to benefit. However, the main impact for
producers arises from the allocated opportunity costs decline (€692 million) due to lower public funding for the public
sector. Excluding this somewhat arbitrary impact item would lead to a negative impact for producers.
Consumers are likely to benefit initially from lower car purchase expenditure – as both the subsidy and reduced
improvement measures reduce their car purchase costs. However, this reduction comes at the cost of higher
expenditure on fuel as fuel consumption of the vehicle fleet is likely to increase compared to the reference scenario.
Overall, increased fuel expenditure outweighs the benefits from increased consumer welfare in respect to car
expenditure. Furthermore, reduced fuel efficiency is likely to lead to higher road tax costs. Finally, consumers are
faced with increased costs for charging infrastructure and grid investments.
The government faces the largest impact from the policy measure as subsidy cost and fuel tax losses significantly
outweigh increased benefits from road tax and electricity taxation. While electricity energy tax and road tax
payments are likely to rise, the policy as a whole is causing a significant net cost of around €3.7billion to the
government between 2012 and 2050.
In conclusion, we find that the policy is leading to some undesired side effects as car producers reduce significantly
the efforts to improve combustion engines. This leads to cost to society due to higher fuel expenditure and
environmental damages thus greatly reducing the benefits that the measure could have otherwise produced. As such,
a policy that aims at directly supporting electric vehicles via subsidies has higher costs than benefits and can
therefore not be recommended.
120
8. Economic impact assessment
8.1. Introduction – Basis set up and requirements
An additional aspect of the policy assessment forms the evaluation of the impact on economic activity and
employment – the so-called economic impact assessment. It provides estimates about the changes in economic output
and corresponding impact on employment demand triggered by a policy intervention. The assessment utilizes a
customized economic model that depicts the economic drivers of the five key sectors that are primarily affected by a
transport system transformation in the form of increased electric mobility. Additionally, the model seeks to replicate
the relationship between those key sectors and the rest of the economy. The sectors identified and modeled in the
economic impact assessment are:
1. Car manufacturing,
2. Car trade,
3. Charging infrastructure,
4. Electricity production and distribution as well as
5. Fuel production and trade.
In order to be able to carry out an assessment of the economic impact of policy measure to requirements need to be
met:
! A detailed picture of how policy influences the main economic drivers such as car technology choice,
car demand, fuel consumption, infrastructure demand etc.
! A suitable economic model that translates the changes in the economic drivers into gross value added
and employment demand.
In respect to the first requirement, MMEM provides an ideal simulation tool to estimate in detail the change in the
economic drivers triggered by a policy intervention. To meet the second requirement we have developed a
customized economic model based on a detailed assessment of the value-chain of the affected industries. The
underlying modeling approach and assumption will be outlined in the following section of the report.
121
Figure&95:&Overview&MMEM&economic&impact&assessment&
Source: MMEM (2011)
8.2. Economic Modeling Approach – Direct impacts
To set up a suitable economic impact model we have paid particular attention to identifying how the value added
shares of car manufacturing differs between conventional and alternative propulsion technologies. For each of the
nine propulsion technology alternatives considered in MMEM we have analyzed the main cost components and
compared the differences across the technologies. This reflects the notion that electric vehicles are based on a
substantially different technology platform compared to existing internal combustion engines. In particular,
production costs (and thus gross value added) are likely to shift away from the actual engine and drive train – typically
the main cost components of vehicles but rather straight forward and low value in electric vehicles – towards the
battery. Indeed, (Blesl M., Bruchof D. et al. 2009) estimate that the battery alone is likely to account for around 60%
of the production costs of battery electric vehicles. The assumptions made in this study are used to estimate the
production costs and productivity parameters in our economic impact assessment (see Table 112).
122
Figure&96:&Breakdown&production&costs&into&cost&components&(2010)&
Source: Blesl M., Bruchof D. et al. 2009
As such, given the changing vehicle component cost, one would expect a significant impact on the economic activity
of the manufacturing sector as soon as electric vehicles achieve a significant market (and production) share. However,
while this may lead to modifications in production technologies an absolute increase or decrease in the economic
contribution depends on two other aspects; the domestic production shares of the new technologies and the labour
requirements (productivity) of the new production techniques.
Regarding the domestic production share, there is yet only anecdotal evidence available. The general notion
proclaimed widely in the industry is that German car manufacturers may not be among the early movers in terms of
investments and production experience in the areas import for alternative technologies – namely battery cell
production, control electronics and electric engines. Following this line, it may be sensible to assume that the
production share will be lower than the current 67% achieved by car manufacturers. Consequently, we assume a
domestic production share of 50% for battery production over the forecasting horizon. Reflecting the uncertainty and
potential impact this should be tested using Monte-Carlo simulation techniques and heuristic probabilities based on
expert reviews.
0%!
10%!
20%!
30%!
40%!
50%!
60%!
70%!
80%!
90%!
100%!
ICE! BEV! FCEV! HEV!(Mild)! PHEV!
AC/DC!changer!
DC/DC!changer!
Fuel!Cell!
Baeerie!
Controlling!electric!
Electric!engine!
Hydrogen!tank!
ICE!fuel!tank!
ICE!
Bodywork!and!drivetrain!
123
Figure&97:&Labour&productivity&(GVA/FTE)&in&€&Tsd.&(2007)&car&manufacturing&components&
Source: Eurostat Structural Business Indicators
The second factor influencing the future economic impact on car manufacturing arising from a shift towards
alternative vehicles is the underlying production technology – in particular the assumed labour and capital
productivity factors. We show the assumption made about productivity for the nine vehicle alternatives. This is based
on an analysis of average productivity in various car manufacturing sub sections. The analysis suggests that, while
differences exist, labour productivity is on the scale of it rather uniform across different sections of car
manufacturing. As such productivity is not the decisive factor determining economic activity and employment demand
in the context of a transport system transformation. The key question is how competitive German car manufacturers
will be and, in turn, if they can defend their overall international market share.
Using an Input-Output-Model we have also attempted to estimate the impact of output changes in the key sector on
inputs from the rest of the economy. For example, this allows estimating how a change in car manufacturing affects
demand for supplies – such as raw materials, parts, machinery and equipment and so on. Overall, our modeling
approach allows assessing both the direct impact on the five key sectors as well as the indirect impact on the rest of
the economy.
EMOB Techno logy c la ss i f i ca t ion
Uni t Gaso l ine
Diesel
Hybr id
LPG_CNG
Biofuel
Hydrogen
BEV PHEV
RE
Average non-bat te ry components
EUR/FTE
46 ,921
46 ,921
46 ,921
46 ,921 46 ,921 51 ,945€ 46 ,677
47 ,305
47 ,305
Bat te ry on ly
EUR/FTE
55 ,300 55 ,300
55 ,300
55 ,300 55 ,300 55 ,300 55 ,300
55 ,300
55 ,300
Table&98:&Productivity&assumptions&by&propulsion&technology&(based&on&2007&data)&
0!
20!
40!
60!
80!
100!
120!
Gross!value!added!per!employee!FTE!
Manufacture!of!motor!vehicles,!trailers!and!semiEtrailers!
Manufacture!of!motor!vehicles!
Manufacture!of!electrical!equipment!for!engines!and!vehicles!n.e.c.!
Manufacture!of!bodies!(coachwork)!for!motor!vehicles,!trailers!and!semiEtrailers!
Manufacture!of!parts,!accessories!for!motor!vehicles!
124
Source: MMEM analysis based on Eurostat Structural Business Indicators
Main industry indicators Unit Sale of motor vehicles
Construc-tion
Produc-tion and distribu-tion of electri-city
Retail sale of automo-tive fuel
Installation of electri-cal wiring and fittings
Manufacture of electri-city distribu-tion and control apparatus
Manufacture of electric domestic applian-ces
Produc-tion of auto-motive fuels
Repair of household applian-ces (O&M of char-ging infra-structure)
Value added share of production value
% 67.4% 38.3% 15.5% 66.2% 43.1% 36.7% 35.8% 2.4% 59.8%
Cost share total personnel costs of turnover
% 7.5% 31.6% 6.1% 6.8% 32.9% 28.8% 22.6% 1.3% 22.7%
Turnover per employee FTE EUR/FTE 382,500 115,374 1,149,806 187,366 100,623 226,840 264,581 7,001,271 133,905
Gross value added per employee FTE
EUR/FTE 65,058 46,500 178,500 32,706 43,800 77,000 70,600 134,000 33,919
Labour cost per employee FTE EUR/FTE 28,615 36,400 69,800 12,689 33,100 65,200 59,700 94,200 16,745
Table&99:&Main&structural&business&indicators&by§or&(2007)&
Source: Eurostat Structural Business Indicators
125
9. Car attributes
9.1. Definition of technologies
Conventional vehicles (ICE)
Conventional vehicles are equipped with an internal combustion engine (ICE) and run on gasoline or diesel.
Battery electric vehicles (BEV)
Battery electric vehicles are equipped with an electric motor and can be charged on public or private charging
stations.
Hybrid electric vehicles (HEV)
Hybrids are equipped with two engines: The internal combustion engine (ICE) and the electric motor are both
connected to the drive train. The electric motor can be charged only by the fuel engine.
Plug-in hybrid electric vehicles (PHEV)
Plug-in hybrid electric vehicles are equipped with an internal combustion engine and an electric motor. PHEVs can be
fueled by a motor fuel (assumed gasoline) and additionally electricity-reloaded on private or public charging stations.
The internal combustion engine and the electric motor are both connected to the drive train.
Range extenders (REV)
Range extenders are equipped with an electric motor and an auxiliary ICE. The electric motor of a RE typically has
more power than in a PHEV. The ICE is not connected to the drivetrain but can only be used to reload the batteries.
Only the electric motor is connected to the wheels.
Biofuel vehicles
This study only focuses on biofuel powered vehicles which consume Bioethanol E85, because to our knowledge only
such biofuel powered passenger cars are sold on the German car market. Bioethanol E85 is a mixture, consisting of
85% bioethanol and 15% gasoline. Since the cars which run on bioethanol E85 are also capable to run on pure gasoline
or mixtures of E85 and gasoline, they are flexible fuel vehicles. Flexible fuel vehicles (FFV) are equipped with only
one ICE like conventional vehicles but can run on more than one sort of fuel, for example biofuel and gasoline.
LPG vehicles
There are mainly two gas technologies, “Compressed natural gas” (CNG) and “Liquefied petroleum gas” (LPG), which
are incompatible for each other. CNG is a fossil fuel. LPG is gained as a by-product by the production of gasoline from
crude oil. CNG is stored in a vehicles tank by high pressure (200-240 bar) while LPG is stored liquid under slight
pressure (10 bar). We decided to scrutinize only one gas technology in our study which is LPG.
Fuel cell electric vehicles (FCEV)
Concerning the Hydrogen technology, we adopt the definition by (The Connecticut Center for Advanced Technology
Inc. 2011)_ENREF_40: Fuel cell vehicles, like electric vehicles, are propelled by electric motors utilizing the fuel cell
to create its own electricity using hydrogen fuel and oxygen from the ambient air.
(The Connecticut Center for Advanced Technology Inc. 2011), p. 7
Plug-in-Hybrids and Range Extenders
126
Both representations of full electric drive hybrids in MMEM are seen as combinations of mild hybrids and pure battery
electric vehicles. Their combined consumptions are obtained by calculating an electric drive share based on actual
mobility patterns (MiD 2008). The electric drive share determines the fraction of electric energy used for propulsion,
it weights to which extend a Plug-in Hybrid or Range Extender are seen as mild hybrids (pure fuel) or battery electric
(pure electricity) vehicles when it comes to consumption.
Initially the available frequency distributions concerning daily travel distances per car are divided into segments
according to KBA, so that eleven independent distributions are formed. The MiD dataset is large enough to supply,
even after data cleansing and segmentation, an average of 1800 occurrences. Comparison of distance travelled and all
electric range allows for calculation of the share of battery-powered travel, assuming prior use of all electric drive.
Summing up these energy shares over all occurrences yields the electric drive share of a segment. Both Plug-in Hybrids
and Range Extenders are methodologically treated the same way.
The initial electric drive share is shown in Table 100.
The above approach reflects expected real life consumptions rather than driving cycle test results. The latter would
be more fit in terms of emission regulation evaluation but are only scarcely available. Considering the fact that
nameplate performance usually overstates real life performance it is to be expected that, once a wider range of full
electric drive hybrids becomes available, average driving cycle consumptions will match or even undermatch MMEM
results.
MMEM-Segment Plug-in Hybrids Range Extenders
Minis 79.6% 94.6%
Kleinwagen 78.4% 93.9%
Kompaktklasse 77.1% 92.9%
Mittelklasse 76.7% 92.1%
Obere Mittelklasse 73.4% 89.7%
Oberklasse 78.2% 91.5%
Geländewagen 82.2% 95.0%
Sportwagen 84.9% 96.5%
Mini Vans 79.0% 94.0%
Großraum Vans 74.2% 91.0%
Utilities 75.4% 91.6%
Table&100:&Electric&drive&share&of&PlugIin&Hybrids&and&Range&Extenders&
9.2. Segmentation
We rely on the segmentation of the Kraftfahrtbundesamt (KBA). The numbers and percentages of new registrations in
each segment are reported in Table 101.
&
Segments Total Private Corp. Total (%)
Private (%)
Corp. (%)
Minis 252589 160248 92341 7.56 9.09 5.58
Kleinwagen 675664 446341 229323 20.35 26.41 13.87
127
Kompaktklasse 901160 472800 428361 27.47 28.86 26.00
Mittelklasse 476676 149559 327117 14.73 9.77 19.91
Obere Mittelklasse 138451 29815 108636 4.32 2.04 6.61
Oberklasse 24652 3154 21499 0.77 0.22 1.31
Geländewagen 259311 118980 140332 8.08 8.01 8.55
Sportwagen 45249 18677 26572 1.42 1.30 1.61
Mini Vans 186767 101129 85638 5.76 6.45 5.15
Großraum Vans 142423 56146 86277 4.39 3.54 5.20
Utilities 141429 49421 92008 4.34 3.09 5.62
Wohnmobile 18873 12055 6818 0.59 0.81 0.41
other 7913 2013 5901 0.24 0.12 0.36
Table 101: New registrations of passenger vehicles in each KBA-segment between 2008 and 2010, Source: Kraftfahrtbundesamt (KBA)
In MMEM the segments “Wohnmobile” and “other” are not considered.
In MMEM the segments are associated with different margins to support the fact that luxury class cars have a higher
margin than vehicles in the economy segment. This plays a role for the purchase price, the optimization submodel,
and the economic impact assessment.
Segment Assumed gross profit margin
Minis 5% Kleinwagen 10% Kompaktklasse 15% Mittelklasse 15% Obere Mittelklasse 15% Oberklasse 20% Geländewagen 20% Sportwagen 20% MiniVans 10% GrossraumVans 10% Util i t ies 10%
Table 102:
9.3. Attributes in buying decisions
In the Market Model Electric Mobility it is assumed that car buyers take into account the following car attributes in
their buying decisions:
! the purchase price of a vehicle,
128
! the fuel costs which are calculated as the product of the fuel price and the fuel consumption of a
vehicle,
! the horse power of a vehicle,
! the range of a vehicle,
! the tightness of the refueling station grid.
The CO2-tailpipe emissions of vehicles are surveyed but turned out to be not significant for buyers’ decisions.
Additionally, the curb weights of vehicles in the different KBA segments are surveyed to draw conclusions about other
attribute values.
Critical points are the valuation of attributes for inexistent or emerging existing technologies which are: Battery
electric vehicles, Hybrids, Plug-in hybrids, Range extenders and Hydrogen vehicles.
Conclusions about the average attribute values of gasoline and diesel cars in the KBA segments are drawn from cars
which are selected as ranking as best sellers in each segment in December 2010 according to ADAC data.
9.3.1. Gasoline cars
Segment according to KBA
Purchase price in €
Fuel consumption in l/100km
Horse power
CO2-emissions in g/km
Tank size in liter
Curb weight in kg
Mini 9940 5,0 60 117 36,50 913
Kleinwagen 11840 5,4 65 126 45,00 1099
Kompaktklasse 16360 6,0 84 139 55,50 1295
Mittelklasse 27790 6,9 130 162 66,00 1455
Obere Mittelklasse 38960 7,5 186 173 66,33 1637
Oberklasse 78090 10,0 299 233 86,00 1915
Geländewagen 39330 9,4 215 220 77,50 1818
Sportwagen 38110 7,7 184 182 70,00 1390
Mini-Van 17750 6,8 105 159 57,00 1378
Großraum-Van 21020 6,8 110 159 59,00 1479
Utility 14550 7,9 80 188 60,00 1420
Table 103: Attribute values of best seller gasoline cars, Source: ADAC search engine requested in Dec. 2010
Segment according to KBA
Purchase price in €
Fuel consumption in l/100km
Horse power
CO2-emissions in g/km
Tank size in liter
Curb weight in kg
Mini 9940 5,0 60 117 36.50 913
Kleinwagen 11840 5,4 65 126 45.00 1099
Kompaktklasse 16360 6,0 84 139 53.50 1295
Mittelklasse 27790 6,9 130 162 63.75 1455
129
Obere Mittelklasse 38960 7,5 186 173 66.33 1637
Oberklasse 78090 10,0 299 233 85.00 1915
Geländewagen 39330 9,4 200 220 77.50 1818
Sportwagen 38110 7,7 184 182 69.50 1390
Mini-Van 17750 6,8 105 159 57.00 1378
Großraum-Van 21020 6,8 110 159 59.00 1479
Utility 14550 7,9 80 188 60.00 1420
Table&104:&&
9.3.2. Diesel cars
Again, the values for the vehicle attr ibutes purchase price, fuel consumption, horse power, range, CO2-emissions and curb weight base on the ADAC search engine, requested in Dec. 2010, for the following car models :
Segment according to KBA
Purchase price in €
Fuel consumption in l/100km
Horse power
CO2-emissions in g/km
Tank size in liter
Curb weight in kg
Mini 11660 3,8 59 100 36,50 950
Kleinwagen 15390 3,8 75 101 45,00 1140
Kompaktklasse 20370 4,8 98 128 53,50 1317
Mittelklasse 30010 4,7 119 124 63,75 1520
Obere Mittelklasse 38940 5,3 145 139 66,33 1673
Oberklasse 73510 7,2 240 189 85,00 1948
Geländewagen 39510 7,2 177 190 77,50 1903
Sportwagen 39410* 5,8* 168* NA 69,50* NA
Mini-Van 20100 4,9 101 127 57,00 1410
Großraum-Van 23180 5,4 100 143 59,00 1576
Utility 15550 6,0 69 159 60,00 1490
Table 105: Attribute values of best seller diesel cars, Source: ADAC search engine requested in Dec. 2010
&
Segment according to KBA
Purchase price in €
Fuel consumption in l/100km
Horse power
CO2-emissions in g/km
Tank size in liter
Curb weight in kg
Mini 11660 3,8 59 100 36,50 930
Kleinwagen 15390 3,8 75 101 45,00 1150
Kompaktklasse 20370 4,8 98 128 53,50 1270
Mittelklasse 30010 4,7 119 124 63,75 1450
Obere Mittelklasse 38940 5,3 145 139 66,33 1800
Oberklasse 73510 7,2 240 189 85,00 1950
130
Geländewagen 39510 7,2 200 190 77,50 1650
Sportwagen 39410* 5,8* 168* NA 69,50* 1600
Mini-Van 20100 4,9 101 127 57,00 1400
Großraum-Van 23180 5,4 100 143 59,00 1500
Utility 15550 6,0 69 159 60,00 1800
Table&106:&&
The attribute values for the diesel sports car are estimated from the attribute values for the gasoline sports car
adjusted by extra charges. The extra charges are estimated from the average excess values of diesel car attributes
compared to gasoline car attributes in the other segments.
9.3.3. Biofuel E85 vehicles
A general overview of the difference between Biofuel E85 cars and conventional cars is given in the following table:
Ford S-MAX 2.0
Flexifuel Titanium (Ethanol-Betrieb)
Volvo S40 2.0F Momentum
Volvo C30 2.0F Kinetic
Ford Galaxy 2.0 Titanium (Ethanol-Betrieb)
Mittelklasse Mittelklasse Mittelklasse
Verbrauch Gesamt l/100km
-2,7 -2,4 -2,5 2,7
Tankgröße l 0 0 0 0
Grundpreis Euro 0 -400 -400 0
Leistung in PS PS 0 0 0 0
Table&107:&Comparison&of&car&attributes&for&gasoline&and&biofuels.&(ADAC&Dec.&2010,&Gasoline&–&biofuels)&
9.3.4. LPG cars
A general overview of the difference between LPG cars and conventional cars is given in the following table:
&
Modell Hyundai i10 1.1 Classic (Autogasbetrieb)
Ford Fiesta 1.4 Titanium (Autogasbetrieb)
Opel Zafira 1.8 Innovation (Autogasbetrieb)
Fahrzeugklasse/ attributes
Kleinstwagen (z.B. Twingo)
Kleinwagen (z.B. Polo)
Untere Mittel-klasse (z.B. Golf)
Verbrauch Gesamt l/100km 1,5 1,4 2,7
Tankgröße l -1,4 -11,4 -16,4
Grundpreis Euro 2700 1890 2200
Leistung in PS PS -3 -4 -3
Table&108:&Differences& in&main&car&attributes&for&LPG&fuel&cars&and&similar&gasoline&cars&(LPG&–&gasoline)&based&on&
ADAC&Autodatenbank&(dic.&2010)&
131
Segment according to KBA
Horse power
Mini 57
Kleinwagen 61
Kompaktklasse 81
Mittelklasse 130
Obere Mittelklasse 186
Oberklasse 299
Geländewagen 200
Sportwagen 184
Mini-Van 105
Großraum-Van 110
Utility 80
Table&109:&Assumed&attribute&values&of&LPG&cars&
9.3.5. Hydrogen cars (FCEV)
Segment according to KBA
Horse power
Tank size in kg
Mini 99 3.4
Kleinwagen 106 3.6
Kompaktklasse 118 4.0
Mittelklasse 136 4.7
Obere Mittelklasse 148 5.1
Oberklasse 197 6.7
Geländewagen 200 6.3
Sportwagen 152 5.2
Mini-Van 134 4.6
Großraum-Van 134 4.6
Utility 156 5.3
Table&110:&Assumed&attribute&values&of&hydrogen&cars&
9.3.6. BEV
&
132
Segment Gasoline curb weight
BEV curb weight 6
Battery weight in kg 7
Battery energy content (kWh)
Vehicle hp
Energy consumption in kWh/100km
Range in km
Battery pack costs in €
Mini 913 1031.7 206 24.8 60 11,7 88 10597
Kleinwagen 1099 1241.9 248 29.8 65 13,1 94 12755
Kompaktklasse 1295 1463.3 293 35.1 84 14,6 99 15030
Mittelklasse 1455 1644.2 329 39.5 130 15,9 103 16887
Obere Mittelklasse
1637 1849.8 370 44.4 186 17,3 106 19000
Oberklasse 1915 2164.0 433 51.9 299 19,5 110 22226
Geländewagen (SUV)
1818 2054.3 411 49.3 200 18,7 109 21100
Sportwagen 1390 1570.7 503 60.3 184 15,4 226 35989
Mini-Van 1378 1557.1 311 37.4 105 15,3 101 15994
Großraum-Van 1479 1671.3 334 40.1 110 16,1 103 17166
Utility 1420 1604.6 321 38.5 80 15,6 102 16481
Table&111:&BEV&characteristics&
& &
6 Estimated by multiplication of gasoline curb weight with factor 1,13 7 Estimated 20 % (Sports: 30%) of BEV curb weight.
133
9.4. Purchase prices
9.4.1. Conventional cars
Init ial purchase prices Initial purchase prices of Gasoline and Diesel cars are estimated from best sellers in each segment according to ADAC.
Evolution of purchase prices: Evolution of purchase prices are calculated as the sum of
! Initial purchase price,
! delayed costs of CO2 reductions,
! delayed costs of emission fines,
! matrix purchase incentive.
9.4.2. Biofuel E85 cars
Based on single model comparisons we assume a 400 € extra purchase price for Biofuel E85 vehicles compared to
Gasoline vehicles.
9.4.3. LPG cars
From model-wise comparisons we assume a 2260 € extra purchase price for LPG vehicles.
9.4.4. Non-internal combustion engine cars
In the Market Model Electric Mobility the purchase prices of non-internal combustion engine cars are calculated as the
sum of the following price components:
! The price of a Gasoline car in the respective segment and time step,
! the non-battery extra price (price differences rooted in assemblies different from Gasoline cars),
! the battery price.
The purchase prices of cars (or their components) are calculated from the production costs of the components, the
margin and the VAT.
It can be assumed that differences in purchase prices of battery electric vehicles and gasoline vehicles are due to
different assemblies.
The purchase prices of PHEV and RE are estimated according to the purchase prices of BEV.
Non-battery extra prices
We rely on the non-battery extra costs based on information reported in (Blesl M., Bruchof D. et al. 2009)
&
BEV Hydrogen (FCEV)
Hybrids (HEV, Full)
RE (PHEV) PHEV*
134
Mini 1088 17883 1875 2023 2563
Kleinwagen 393 21386 1425 1513 2177
Kompaktklasse -339 25078 952 976 1770
Mittelklasse -937 28091 565 538 1438
Obere Mittelklasse -1617 31519 125 39 1060
Oberklasse -2656 36755 -547 -722 483
Geländewagen -2293 34928 -312 -457 685
Sportwagen -694 26867 722 716 1573
Mini-Van -649 26641 751 749 1598
Großraum-Van -1027 28543 507 472 1388
Utility -806 27432 650 634 1511
Table&112:&NonIbattery&extra&costs&calculated&from&(Blesl&M.,&Bruchof&D.&et&al.&2009)&
To calculate the extra prices from extra costs we add the corresponding segment margin (between 5% and 20%) and
the VAT.
The calculations of the numbers given in Table 112 are explained as follows:
(Blesl M., Bruchof D. et al. 2009) assume that the manufacturing costs and curb weights of some vehicle components
are segment or technology dependent while others are not. (Blesl M., Bruchof D. et al. 2009) pp. 72 - 73 provide data
about assembly manufacturing costs for vehicles of different technologies only for Kleinstwagen and Mittelklasse
cars.8 From these data we estimate assembly costs for vehicles in other KBA segments than Minis or Kleinstwagen.
Therefore we assume that the assembly costs for cars in other segments increase proportional to the median curb
weight of the vehicles compared to the segments Kleinstwagen and Mittelklasse:
!!,!,! = !!!!"#",!!!!,! +!!!!"##$%&%'(($,!!!!,! − !!!!"#",!!!!,!!!!!"##$%&%'(($,!!!!,! − !!!!"#",!!!!,!
�
#(!!,!,! − !!!!"#",!!!!,!)!∀!!, !, !.#
(17)!
and
! c!,!,! are the manufacturing costs of the assembly i of a technology t vehicle in the KBA segment s,
! w!,!,! is the weight of the assembly i of a technology t vehicle in the KBA segment s,
! s ∈ (Mini, Kleinwagen, Kompaktklasse,…) is a label for the KBA segment,
! t! ∈ (BEV,Hydrogen,… ) is a label for the technology,
! i! ∈ (combustion!engine, electric!motor,… ) is a label for the technology.
In Market Model Electric Mobility the manufacturing costs for vehicle components of different KBA segment vehicles
are estimated according to (Blesl M., Bruchof D. et al. 2009) to be proportional to the average weight of best sellers
in each segment reported by ADAC. Considering the details provided by this source of data it is used in the MMEM
modeling.
8!! It!is!assumed!that!the!definition!of!the!segment!Kleinstwagen!corresponds!to!the!ADAC!definition!of!a!Mini.!Also! the! Blesl! M.,! Bruchof! D.,! et! al.! (2009).! Entwicklungsstand! und! Perspektiven! der! Elektromobilität,! Universität!Stuttgart,!Institut!für!Energiewirtschaft!und!Rationelle!Energieanwendung:&78.!definition!of!the!technologies!FCEV,!Full!HEV!and!PHEV!correspond!to!the!MMEM!technology!definitions!of!Hydrogen,!Hybrids!and!RE.!
135
The estimation of non-battery costs of such vehicles is based on a cost increase compared to conventional Gasoline
vehicles. To calculate the non-battery related purchase price differences, the manufacturing costs are multiplied by a
margin and the VAT.
Calculation tables can be found in the appendix.
9.4.5. Battery prices
The battery prices are calculated as the product of the battery kWh unit prices reported in 10.3 and the energy
contents (in kWh) of a battery pack assumed for the respective technologies and segments.
9.5. Fuel costs and fuel consumptions
(Blesl M., Bruchof D. et al. 2009), p. 33 report energy consumptions in MJ/km for Kleinstwagen and
Mittelklassewagen. The values are according to the directive of the "Neue Europäische Fahrzyklus” (NEFZ) and
adjusted by 10 %, since they claim that real consumptions are about 10% higher:
&
technologies/ segments Reference vehicle
gasoline
Reference vehicle
diesel
BEV Hydrogen (FCEV)
Hybrids (HEV, Full)
RE, electr. (PHEV)
RE, conv. (PHEV)
percent of gasoline consumption 35 71 79 35 71
Mini 1.68 1.57 0.58 1.20 1.32 0.58 1.20
Kleinwagen 1.83 1.70 0.63 1.26 1.49 0.63 1.31
Kompaktklasse 1.99 1.85 0.69 1.32 1.67 0.69 1.43
Mittelklasse 2.11 1.97 0.73 1.37 1.82 0.73 1.53
Obere Mittelklasse 2.26 2.11 0.78 1.43 1.99 0.78 1.64
Oberklasse 2.48 2.31 0.86 1.51 2.24 0.86 1.81
Geländewagen 2.41 2.24 0.83 1.48 2.15 0.83 1.75
Sportwagen 2.06 1.92 0.71 1.35 1.76 0.71 1.49
Mini-Van 2.05 1.91 0.71 1.35 1.75 0.71 1.48
Großraum-Van 2.13 1.99 0.74 1.38 1.84 0.74 1.54
Utility 2.09 1.94 0.72 1.36 1.79 0.72 1.51
Table& 113:& Energy& consumption& in&MJ/km,& bold& numbers& are& based& on& (Blesl&M.,& Bruchof& D.& et& al.& 2009),& p.& 33,&
remaining&numbers&are&interpolated&
&
9.5.1. Conventional cars
The fuel costs (€/100 km) for Gasoline and Diesel cars are calculated as the product of fuel prices (€/l) and fuel consumptions ( l/100 km): Fuel costs (€/100 km) = fuel price (€/l) × fuel consumption (l/100 km)! (18)!
136
For initial fuel consumptions of Gasoline and Diesel cars we rely on data from the ADAC search engine. (Gasoline and
Diesel best sellers in each segment according to ADAC search engine). Since the ADAC database consists of data about
fuel consumptions reported by car manufacturers we believe that these fuel consumptions may be the case for test
driving cycles but not for real driving routes.9 So we checked the ADAC data with consumers’ statements about real
consumptions of individual cars. Therefore we gathered information from the website “Spritmonitor.de”, see 12.2.
We conclude that the fuel consumptions of Gasoline and Diesel cars are between 20 % and 33 % higher than reported
by the ADAC.
As a comparison (VCÖ) and (VCÖ 2011) report that the magazine „Auto, Motor und Sport“ which tests new cars on a
regularly basis, found in an analysis of 216 tests conducted in 2010 that the actual fuel consumptions of passenger cars
were on average one third higher than it was stated from the respective manufacturers.
It is assumed that the average fuel consumption of vehicles in a specific segment decreases because of technological
improvements in time.
For example (ADAC 2011) reports:
Die heute in Deutschland zugelassenen Pkw verbrauchen im Schnitt knapp ein Viertel weniger Kraftstoff als die Wagen vor 30 Jahren.
Wie die Auswertung von Zahlen des Deutschen Instituts für Wirtschaftsforschung (DIW) zeigt, lag im Jahr 1979 der Durchschnittsverbrauch von Pkw noch bei zehn Litern je 100 Kilometer. Bis 2008 sank er um 24 Prozent auf durchschnittlich 7,6 Liter.
Diesel-Pkw verbrauchten 2008 im Durchschnitt 6,9 Liter und damit 2,1 Liter weniger als 1979. Damals schluckten die Selbstzünder noch neun Liter. Nicht ganz so stark reduzierte sich der durchschnittliche Kraftstoff-Konsum von Benzinern. 1979 mussten die Autofahrer 10,1 Liter Benzin je 100 Kilometer veranschlagen. 2008 reichten für die gleiche Strecke 8,2 Liter.
The fuel consumption in MMEM in a time step is based on: Initial fuel consumption × ICE consumption index. The ICE
consumption index compares CAFÉ optimization, CO2 emissions and initial CO2 emissions.
As a comparison (Blesl M., Bruchof D. et al. 2009), p. 32 take the “Smart for Two Coupé” and the “VW Golf Diesel” as
Kleinstwagen Gasoline and Mittelklasse Diesel reference vehicles. They claim that the fuel consumptions of their
reference vehicles will decline by about 21 percent until 2030.
9.5.2. BEV
The fuel costs or in the BEV case energy costs (€/100 km) are calculated as the product of the consumed energy
(kWh/100 km) and the energy price (€/kWh).
Consumed energy We take several information sources into account, see appendix 12.3.6, and conclude from (Rousseau, Shidore et al.
2007), p. 4 that an electric vehicle of 1500 kg curb weight consumes 143 Wh/km and additional 6,5 Wh/km for each
100 kg curb weight in excess of 1500 kg. These values are used to take into account the different energy requirements
of the cars of different segments. Concerning the curb weights of BEV in different segments we rely on our estimates.
9! Presumably! the!manufacturers! provide! results! about! fuel! consumptions! from! the! “Neue! Europäische! Fahrzyklus“!(NEFZ).!
137
Additionally, losses of energy during recharging are considered. We assume that one third of the charging energy gets
lost during a charging operation.10 So it is concluded that the energy requirements of electric vehicles have to be
multiplied by the factor 1,41 to take energy losses during the loading operations into account.
All in all in the Market Model Electric Mobility the energy consumptions of BEV is calculated as: 11 BEV!energy!consumption!(kWh/100!km)!=!1,41!×![14,3!kWh/100!km!+!0,65!kWh/100!km!×!(BEV!weight!in!kg!–!1500!kg)/!100!kg]!
(19)!
9.5.3. Hybrids
Based on the analysis of ADAC data, it appears that Hybrid consumption typically represent 70% of gasoline fuel
consumption. (Nemry and Leduc 2009), p. 20 report an average fuel consumption of 5,13 liter/100 km for a Hybrid
reference vehicle and 7,26 liter/100 km for an ICE reference vehicle.
From these both information sources we conclude that Hybrids consume 30 percent less gasoline than comparable Gasoline cars: Hybrid gasoline consumption (l/100 km) = 0,7 × ICE gasoline consumption (l/100 km)
Again, to consider fuel consumptions under real driving conditions we
assume an increased fuel consumption by a factor of 19 % comparable to
Gasoline cars.
(20)
9.5.4. PHEV and RE
We assume that if PHEV and RE are running on fuel (which we assume is Gasoline) their consumption is similar to
Hybrids (with the possible provision that it is increased by the extra weight of batteries):
PHEV fuel consumption in l/100km = Hybrid fuel consumption in l/100km ×
((battery capacity in kWh / 0,12 kWh/kg) + weight of gasoline car in the
segment in kg) / weight of gasoline car in the segment in kg.
Again,! to! consider! fuel! consumptions! under! real! driving! conditions! we!assume! increased! fuel! consumption! by! a! factor! of! 19! %! comparable! to!Gasoline!cars.!
(21)!
And if PHEV and RE are running on electricity their consumption is similar to BEVs:
PHEV energy consumption in kWh/100km = Hybrid energy consumption in
kWh/100km + 0,65 kWh/100 km × (battery capacity in kWh / 0,12 kWh/kg)
/100 kg (additional vehicle mass)!
(22)!
Again we consider energy losses during the charging operation:
10 auto-motor-und-sport.de (2010). "Smart Fortwo Electric Drive im Praxistest." reports about the recharging of the Smart Fortwo
Electric Drive: “Um den 16,5 Kilowattstunden großen Stromspeicher zu füllen, müssen 23,3 Kilowattstunden hineingesteckt werden. Fast sieben davon versiegen also beim Laden, so als würde beim Tanken ein Drittel des Kraftstoffs gebraucht, um die Zapfpistole zu betreiben.“
11 Instead of curb weight, the weight under driving conditions would be more appropriate for the calculation of energy consumptions, but there are no data available for this study.
138
Energy!losses!in kWh100km != �!�!PHEV!energy!consumption!in!kWh/100km! (23)!
and f is a factor whose value is defined in section 0.
9.5.5. Hydrogen vehicles
(Thomas 2000), s l ide 2 report a mass energy density of 120 MJ/kg hydrogen. So for hydrogen vehicles the fol lowing consumptions are estimated: Table&114:&
Segment Hydrogen consumption in kg/100 km
Mini 1.00
Kleinwagen 1.05
Kompaktklasse 1.10
Mittelklasse 1.14
Obere Mittelklasse 1.19
Oberklasse 1.26
Geländewagen 1.23
Sportwagen 1.13
Mini-Van 1.13
Großraum-Van 1.15
Utility 1.13
Table&115:&Assumed&energy&consumptions&of&hydrogen&cars&&
(TCS), p. 1 reports: “Der Energieaufwand für die Komprimierung auf 700 bar beträgt etwa 12% des Wasserstoff-
Energiegehalts. Der Energieaufwand für die Verflüssigung von Wasserstoff ist noch grösser.” Again,! to! consider! fuel!consumptions!under!real!driving!conditions!we!assume!increased!fuel!consumption!by!a!factor!of!19!%!comparable!to!Gasoline!cars.
9.5.6. Biofuel E85 vehicles
From several information sources (see appendix 12.7) we conclude that the consumption of E85 vehicles is about 30 % higher than the consumption of Gasoline cars: E85!consumption!(l/100!km)!=!1,3!×!ICE!gasoline!consumption!(l/100!km)! (24)!
While!E85!vehicles!are!flexible!fuel!vehicles!we!assume!that!drivers!only!run!on!E85!and!not!on!Gasoline!or!other!fuels.!
Again,!to!consider!fuel!consumptions!under!real!driving!conditions!we!assume!increased!fuel!consumption!by!a!factor!of!19!%!comparable!to!Gasoline!cars.!
139
9.5.7. LPG vehicles
We estimate an increased consumption of about 20 % compared to Gasoline vehicles: LPG consumption ( l/100 km) = 1,2 × ICE gasoline consumption (l/100 km) (11) This formula is based on engineering data avai lable and appears consistent with information collected about the difference between similar models branded together as Gasoline and Gas versions. Again,! to! consider! fuel! consumptions!under! real! driving! conditions!we!assume!an! increased! fuel! consumption!by!a!factor!of!19!%!comparable!to!Gasoline!cars.!
9.6. Horsepower
9.6.1. Conventional vehicles and BEV
Horsepower (HP) values of best seller Gasoline and Diesel cars are obtained from the ADAC search engine. The HP in
each segment is assumed to remain unchanged in time. The horsepower of a BEV is assumed to be comparable to a
Gasoline car in the same segment.
9.6.2. Hybrids
We tried to make attribute comparisons for Gasoline and Hybrid vehicles. But so far comparable car models are
scarce. So we rely on the Toyota Auris vehicle which exists in a Gasoline and a Hybrid version. Toyota reports 132 HP
for the Gasoline vehicle and a system power of 136 PS for the Hybrid. The system power of the Toyota Auris Hybrid
can be decomposed into 99 HP for the combustion engine and 82 HP for the electric motor. (Toyota Deutschland
GmbH 2011)
So we conclude that the HP of a Hybrid equates to the HP of a Gasoline vehicle in the same segment.
9.6.3. PHEV and RE
To draw conclusions about the horse powers of PHEV and RE we rely on a comparison of the HP values of the Toyota
Prius Plug-in Hybrid and the Toyota Prius Hybrid.12 In a telephone conversation Toyota reports for both vehicles a
system power of 136 PS and a maximum speed of 180 km/h.
In MMEM it is assumed that the HP of PHEV and RE amounts to the same hp value of Hybrids.
9.6.4. Hydrogen cars
HP values are as listed.
12 The Toyota Prius Hybrid is segmented by the KBA as a compact car. The cylinder capacity of the Toyota Prius amounts 1800 cm3
and has a length of 4460 mm. The Hybrid is equipped with an 82 PS (60 kW) electric engine and a 99 PS (73 kW) gasoline engine and a 202 Volt-battery (NiMH-Akkus) with a power of 27kW. The PHEV is equipped with an 82 PS (60 kW) electric engine and a 76 PS (56 kW) gasoline engine and a Kobalt-Li-ion-battery (288 units). The Prius Hybrid can drive up to 45 km/h and the Prius plug-in hybrid up to 70 km/h in an all-electric mode. The Plug-in battery has 96 cells á 3,6 Volt, summed to 345,6 Volt and 5,2 kWh.
!
140
9.6.5. Biofuel E85 cars
We consider several information sources. We bel ieve (Empa Dübendorf 2007) and assume an HP increase of 5 percent compared to gasoline vehicles. Hp#(PHEV#or#RE)#=#1,05#×!hp#(Hybrid)# (25)!
9.6.6. LPG cars
We assume the HP values as listed in ADAC, which deviate from assumptions about Gasoline cars only for the segments
Minis, Kleinwagen and Kompaktklasse.
9.7. CO2 – tailpipe emissions
9.7.1. Conventional vehicles
Based on fuel consumption and the CO2 content of the different combustion processes OR when CAFÉ optimization is
running based on the output of CAFÉ optimization.
9.7.2. Hybrids
based on fuel consumption
9.7.3. Range Extenders
The CO2-emissions measured in gram/km of Range extender cars are assumed to be the same as the CO2-emissions of
Gasoline cars in the respective segment less 50 gram/km.
9.8. Range
9.8.1. Conventional vehicles
The range of a Gasoline or Diesel vehicle in a segment is estimated by the fraction of the tank size and the Gasoline or
Diesel fuel consumption of the car:
Range (km) = tank size (l) / fuel consumption (l/km).! (26)!
Tank sizes and fuel consumptions for Gasoline and Diesel vehicles are listed in ADAC list of models. Statements about
fuel consumptions under real drive conditions in 0 are to be considered.
9.8.2. BEV
The range of a BEV (km) is calculated from the chargeable energy content of its battery pack (kWh), the SOC-window
(%) for batteries and the electricity consumption (kWh/km):
Range (km) = battery pack energy content (kWh) × SOC-window (%) /
energy consumption (kWh/km)
(27)!
141
The battery pack energy content is listed. The SOC-window is described in
10.2 and the energy consumption in 9.5.2.!
9.8.3. Hybrids
In the MMEM it is assumed that currently manufactured Hybrids are based on conventional Gasoline or Diesel vehicles
in their basic equipment. Assuming that the tank size stays the same compared to a Gasoline car, the range will be
enlarged because of the lower fuel consumption of Hybrids, see 9.5.3. According to the fuel reduction the range of a
Hybrid in a segment is calculated by the range of a Gasoline vehicle divided by 0,7:
Hybrid range (km) = Gasoline vehicle range (km) / 0,7! (28)!
Additionally the range of a Hybrid is slightly increased by the available energy from the battery. This is calculated
according to (27).
9.8.4. PHEV and RE
The range of a PHEV or RE is determined by the range based on fuel use and the all-electric range (AER).13
Range based on fuel use:
Basically, it can be assumed that the tank volume stays the same compared to a Hybrid or Gasoline car. This is
confirmed by vehicle comparisons. For example, the tank volume of the Gasoline car BMW 850i amounts 82 liter,
while the tank volumes of the BMW Active Hybrid X6 and the BMW Active Hybrid 7 amount 85 and 80 liters.
PHEV!/!RE!range!(km)!=!Hybrid!range!(km)! (29)
All-electric range:
The AER (km) is calculated as the ratio of the battery size (kWh) to the battery consumption (kWh/km).
PHEV! /! RE! AER! (km)! =! battery! capacity! (kWh)! ×! SOCErange! (%)! /! electric!consumption!(kWh/km)!
(30)!
9.8.5. Hydrogen vehicles
(Nationale Plattform Elektromobilität 2010), p. 15, reports ranges for Hydrogen vehicles between 400 and 600 km.
The range of a Hydrogen vehicle is calculated from the tank size and the hydrogen consumption (reported in 9.5.5).
Hydrogen range (km) = Hydrogen tank size (kg) / Hydrogen consumption
(kg/100 km)!(31)!
9.8.6. Biofuel E85 vehicles
13 For comparison Toyota reports an autonomy of 1150 km (Pressemitteilung der Toyota Deutschland GmbH vom 14. Mai 2010 2010).
142
Range is computed based on a tank size (similar to gasoline) and fuel consumption. Since the fuel consumption of
Biofuel propelled vehicles is taken to be 30 percent higher compared to Gasoline vehicles, the range of Biofuel
vehicles is calculated by division of the Gasoline by 1,3.
E85 vehicle range (km) = Gasoline vehicle range (km) / 1,3! (32)!
9.8.7. LPG vehicles
From model comparisons, we conclude that the tank size of LPG vehicles is on average 9 liter less than the tank size
of a comparable gasoline car.
143
10. Battery technologies
The battery technology of an electric vehicle influences its car attributes which are crucial for consumers’ vehicle
choice decisions. So it is assumed that conclusions about characteristics of current and future electric vehicles can be
drawn from information about currently available and projected future battery technologies.
In the recent years more often batteries of the Lithium-ion (Li-ion) technology are assembled in electric vehicles.
Apparently, the resource Lithium is more appropriate for batteries than any resources used for precedent battery
technologies. (Nemry and Leduc 2009) report:
Car makers are moving to lithium-ion batteries, especially because they offer energy density higher than what NiMH batteries do. They are also characterized by the absence of memory effects and low self-discharge rate. They are seen as the best option to meet the energy storage requirements not only for PHEVs, but also for BEVs and HEVs, at least in the short to medium term.
(Nemry and Leduc 2009), p. 10
(Lowe, Tokuoka et al. 2010), pp. 12-13 show that the resource Lithium allows a wide range of configurations of
battery attributes (concerning the attributes energy content in kWh, power in kW, SOC-range in percent) compared to
resources like Nickel-Metallhydrid (NiMH) used for precedent battery technologies. While (Deutsche Bank 2009), p. 42
reports that “there are four main types of automotive lithium ion batteries” available, we focus on one hypothetical
Li-ion technology with a special configuration of battery attributes. Since Li-ion batteries have a large potential for a
further increase of energy density by using advanced anode and cathode materials, see (Lowe, Tokuoka et al. 2010),
p. 13, it is assumed that not only current but also future batteries rely on the Li-ion technology. This assumption is
even made for hybrids which are still often equipped with NiMH batteries.
For our research purposes a battery technology is well-defined by the energy content that means chargeable capacity
per battery unit weight (kWh/kg), the battery power per battery unit weight (kW/kg), and the usable energy content
that means state-of-charge (SOC) window of a battery:
! Energy content (kWh/kg),
! Battery power (kW/kg),
! State-of-charge (SOC) window (%).
Further, it is assumed that battery weights vary across car technologies (BEV, PHEV, RE and Hybrids) and car
segments. In MMEM the segmentation of the German Kraftfahrtbundesamt (KBA) is used.
In the following sections our conclusions about the values of battery attributes for our hypothetical Li-ion technology
are described. Information from which the conclusions are drawn is listed in the appendix.
10.1. Energy content per battery pack and weight
The energy content (kWh) of a battery unit (one battery pack) is calculated as the product of
! the energy content per battery unit weight (kWh/kg) and
! the battery unit weight (kg).
We assume that the energy content per battery unit weight (kWh/kg) increases in time. The battery weight (kg) is
technology and segment dependent. In the reference case it is assumed that technology improvements are converted
into lighter batteries while the energy content (kWh) of a battery pack stays constant in time.
144
The assumed weights (kg) of battery packs for different car technologies and segments are specified in the respective
car technology sections. These values for assumed battery weights are taken as initial values.
The assumed energy content per battery unit weight (kWh/kg) is assumed as follows:
Init ial values (Lowe, Tokuoka et al. 2010), p. 13 report a chargeable electric energy content of 120 Wh/kg for Lithium-Ion
batteries. We rely on this value taking also into account information from other studies listed in 12.3. This value also
bears up against several reports about batteries of BEV.
Evolution of values MMEM reflects the common expectation that battery technologies will improve in time to continue the historic trend
in the sense that the energy content measured in kWh/kg will increase in time. Proceedings in battery technology
improvements can be translated in a production of lighter batteries while keeping the energy content in kWh/battery
constant compared to batteries of current technologies (MMEM reference case). Or improvements in battery
technologies can be translated in the manufacturing of batteries with higher energy contents in kWh while the battery
weight in kg stays constant compared to batteries of current battery technologies. A more plausible third option is
that a part of the technology improvements will be translated into lighter batteries and the other part into a higher
energy density. Also it can be assumed that manufacturers will track different strategies of translating battery
technology improvements in batteries of higher density and less weight compared to batteries of current technologies.
Concerning the development of the energy content per battery unit weight (kWh/kg) we rely on a scenario reported in
(Nationale Plattform Elektromobilität 2010), p. 8.
Time kWh/kg
1.1.2010 0,12
1.1.2014 0,12
1.1.2017 0,125
1.1.2020 0,17
Table 116: NPE-scenario for the development of the energy content per battery unit weight, Source: (Nationale Plattform Elektromobilität 2010), p. 8
In the MMEM reference scenario we rely on the values of 0,12 kWh/kg for 1.1.2010 and 0,17 kWh/kg for 1.1.2020.
Deviating from the NPE scenario we assume that values for intermediate points in time or beyond 2020 are linearly
interpolated resulting in:
&
Time kWh/kg
1.1.2010 0,12
1.1.2020 0,17
1.1.2051 0,44
Table&117:&MMEM&reference& scenario&of& the&energy& content&per&battery&unit&weight& for&different&points& in& time,&
based&on&(Nationale&Plattform&Elektromobilität&2010),&p.&8&
145
Technology improvements are translated into batteries with constant energy content (kWh) and decreasing weight
(kg).
In an alternative scenario we assume that the value for 2020 stays constant until 2050. So the following energy
densities are assumed:
Time kWh/kg
1.1.2010 0,12
1.1.2014 0,12
1.1.2017 0,125
1.1.2020 0,17
1.1.2051 0,17
Table&118:&MMEM&alt.&scenario&of&the&energy&content&per&battery&unit&weight&for&different&points&in&time,&based&on&
(Nationale&Plattform&Elektromobilität&2010),&p.&8&&&
In MMEM values for intermediate time points are linearly interpolated.
Again we rely on (Lowe, Tokuoka et al. 2010), p. 13 which report a battery power of 1800 W/kg and refer to section 0
for an overview about further studies. While we think the battery power is an important determinant of a battery, it
is of no more relevance for our further considerations.
10.2. State-of-charge window
It can be differentiated between the nameplate and the available (or rechargeable) electric energy content of a
battery: Only a fraction (the so-called “State-of-charge (SOC) window”) of the nameplate energy content of a battery
is available. (National Research Council 2010) explains:
A rechargeable battery can be charged to 100 percent of its capacity and then discharged to zero percent, but full charge would not allow regenerative braking, and full discharge typically would seriously damage its future performance. Early PHEV batteries may be limited to 80 percent of full charge and prevented from discharging to less than 30 percent. This is a 50 percent SOC [state-of-charge] range.
(National Research Council 2010), p. 7, footnote 1
Other information sources report different SOC windows, see 12.3.1, from which we conclude that a 60 percent SOC
window describes a hypothetical Li-ion battery technology for BEV, PHEV and RE best. Since charging cycles of BEV,
PHEV and RE are deep compared to charging cycles of Hybrids, it can be assumed that a different Li-ion battery
technology is applied for Hybrids with a narrower SOC window.14 While the SOC window is of relevance for the
calculation of the range of BEV, PHEV and RE this is not the case or negligible for Hybrids. So in MMEM there is no
further consideration about the SOC range for hybrids.
Taking the partially contradictory information (listed in the appendix 12.3.1) into account, we assume that the
available energy (or state-of-charge window) will be 60 percent of the battery capacity for a Lithium-ion battery for
BEV, PHEV and RE.
10.3. Battery kWh unit costs 14 Deutsche Bank (2009). Autos & Auto Parts Electric Cars: Plugged In 2, Deutsche Bank. assumes different battery technologies for
hybrids compared to other battery technologies.
146
Since a time trend of declining manufacturing costs for Li-ion batteries can be observed, the evolution of battery costs
or prices (incl. margin and VAT) is considered.
Init ial values Based on the information sources listed in appendix 12.3.2 we rely on a battery cost estimate of 620 €/kwh nameplate
capacity for BEV, PHEV and RE. Relying on (Deutsche Bank 2009) we further assume 40 percent higher battery unit
costs for Hybrids.
Additional to the battery production costs we take into account a 35 percent increase corresponding to VAT,
assembly, commercial margin, thus leading to a battery unit price of about 840 €/kwh nameplate capacity for BEV,
PHEV and RE and 1176 €/kWh nameplate capacity for hybrids.
Evolution of values Concerning the development of the battery kWh unit costs we rely on a NPE forecast until 2020 and extrapolate this
time series for time points beyond 2020:
&
Date €/kWh
1.1.2010 850
1.1.2011 800
1.1.2012 500
1.1.2013 450
1.1.2014 400
1.1.2015 380
1.1.2016 350
1.1.2017 300
1.1.2018 290
1.1.2019 285
1.1.2020 280
1.1.2050 174
1.1.2052 174
Table 119: Battery kWh unit price development, Source: NPE until 2020 and own assumptions beyond 2020
We use these values in our reference scenario, so the value for 2010 slightly deviates from our own findings of 840
€/kWh. In alternative scenarios (applying for example a 2-Factor-Learning-Curve) we rely on our own finding of 840
€/kWh for 2011 and assume other values for the development of the battery price.
10.4. Battery weights and capacities
The battery pack contributes significantly to the manufacturing costs of an electric vehicle. Manufacturers reports
about projected BEV indicate that the battery sizes vary across technologies (BEV, PHEV, RE and Hybrids) and
segments (Minis, Compact cars, Utilities, etc.). Even though some manufacturers intend to produce smaller vehicles
equipped with high energy capacity batteries (for example the BMW Mini), it seems to be reasonable that bigger and
heavier vehicles which are appropriate for longer journey distances, are also assembled with higher energy capacity
batteries. We think that the assumption of different average battery sizes across segments is plausible since it can be
147
assumed that for higher class vehicles a wider range is demanded by consumers. In addition, the required energy of a
vehicle (in a driving cycle) depends on its weight, see (Rousseau, Shidore et al. 2007), p. 4. So we assume the weight
of a vehicle’s battery to be proportional to the vehicle’s curb weight. The curb weight of a BEV in turn is assumed to
be proportional to the curb weight of a gasoline car in a segment.
BEV battery weights and capacit ies Empirical data about curb weights and battery weights of several BEV are gathered to calculate the average curb
weight of a BEV in a segment and the average percentage ratio of the battery weight to the curb weight.
Generally, comparing BEV with gasoline cars in a segment we find that the curb weight of a BEV increases by 13%.15
We assign this increase in weight basically to the heavy battery of a BEV but we also take into consideration that an
electric motor weights less than an internal combustion engine. We abstract from differences in weights between BEV
and Gasoline vehicles resulting from other assembly differences.
We conclude that BEV are heavier than Gasoline vehicles in a segment but the curb weight of a BEV reduced by its
battery weight amounts less than the curb weight of an ICE vehicle.
Empirical data additionally suggest that the battery weights around 20 % of the BEV vehicle weight for all segments
but 30 % for roadsters,
BEV weight (kg) = 1,13 ×!gasoline vehicle weight (kg)! !(33)!
battery weight (kg) = curb weight (kg) × 0,2 (all segments, but for sports
factor 0,3)
!(34)!
Following this approach the battery capacities in kWh are calculated as the product of the battery weights in kg for
each segment and the unit energy content in kWh/kg. The assumed battery capacities for different segment BEVs are
reported in BEV characteristics.
battery#pack#energy#content#(kWh)#=#battery#weight#(kg)#×#0,12#(kWh/kg);#As a comparison (AECOM Australia 2009), p. 10 assumes a battery pack capacity of 40 kWh for BEV but 25 kWh for
small BEV as initial values. (Nemry and Leduc 2009), p. 8f., assume 30-50 kWh for BEVs.
Hybrids battery capacit ies Concerning Hybrids we have access to market data about their battery capacities:
! The Audi A6 Hybrid (segmented as “Obere Mittelklasse”) is equipped with a Lithium Ion-battery (1,3
kWh and 39 kW).
! The Toyota Prius Hybrid (segmented as “Kompaktklasse” in the ADAC data base, but more alike to a
“Mittelklasse” vehicle) is endowed with a Nickel-Metallhydrid (NiMH)-battery (49,5 kg; 1,3 kWh and 27
kW).16
Further we take into account information from the following reports:
! (U.S. Department of Energy 2007) reports a capacity of 1 to 2 kWh for Hybrid batteries.17
! (Deutsche Bank 2009), p. 49 assumes a 2 kWh battery pack for a ‘Full HEV’.
15 As a comparison VCÖ (2010), p. 19 finds that electric vehicles are nearly 20% heavier than comparable conventional vehicles. We
find the same percentage increase from our empirical data about BEV. 16 see cleanfleetreport.com, cleanfleetreport.com. 17 US Department of Energy (2007). Plug-in Hybrid electric vehicles R&D Plan.
148
Since it can be assumed that NiMH-batteries which are currently often assembled in Hybrids will be replaced by Li-ion
batteries in the short to medium term, we also refer to the Li-ion technology with regard to Hybrids.
In our model it is assumed that Hybrids are equipped with 1,3 kWh Li-ion battery packs as initial value.
PHEV and RE battery capacit ies According to our considerations about BEV we believe that the battery sizes of PHEV and RE depend on the
segmentation of a vehicle. The average sizes of batteries of PHEV and RE in each car segment are assumed to be
proportional to the gasoline consumption of a gasoline car in the same segment. Further we rely on (Faron, Pagerit et
al. 2009) in that the energy content of an average battery amounts 6 kWh for PHEV and 14 kWh for RE. We take these
values for compact cars and calculate segment specific battery capacities:
Battery!size!of!a!car!in!segment!i! in!kWh= gasoline!consumption!of!a!car!in!segment!i! in!l !×!battery!size!of!a!compact!car! 6!or!14!kWh/gasoline!consumption!of!a!compact!car! 6!l
(35)!
An application of this formula yields: &
Segment Gasoline liter consumption
PHEV battery capacity (kWh)
RE battery capacity (kWh)
Minis 5.0 5.0 11.7
Kleinwagen 5.4 5.4 12.6
Kompaktklasse 6.0 6.0 14.0
Mittelklasse 6.9 6.9 16.1
Obere Mittelklasse 7.5 7.5 17.5
Oberklasse 10.0 10.0 23.3
Geländewagen 9.4 9.4 21.9
Sportwagen 7.7 7.7 18.0
Mini-Vans 6.8 6.8 15.9
Großraum-Vans 6.8 6.8 15.9
Utilities 7.9 7.9 18.4
Table&120:&Assumed&PHEV&and&RE&battery&capacities&(kWh)&
10.5. Battery pack price
The price of a battery pack (€) is calculated in MMEM as the product of
! the battery weight (kg),
! the (nameplate) energy content per kg battery (kWh/kg),
! the battery kWh unit costs (€/kWh), see section 10.3,
! the VAT, assembly and commercial margin.
149
150
11. Fuel costs
11.1. Electricity prices in MMEM
In Market Model Electric Mobility we take into consideration different scenarios for the future development of
electricity prices.
In the reference scenario we take as an electricity price forecast the (Prognos-EWI-GWS 2010), p. 238 scenario.
&
Established in time Value (€/kWh)
1.1.2010 0,22
1.1.2020 0,22
1.1.2030 0,227
1.1.2040 0,22
1.1.2050 0,215
1.1.2051 0,215
Table 121: Electricity price reference scenario, Source: (Prognos-EWI-GWS 2010)
11.2. Oil price
Prices for fuels are linked to the oil price. In a basic scenario we assume the following development of the oil price:
We rely on EIA oil price scenarios for which time series in annual periodicity from 1.1.2011 to 1.1.2051 are on hand.
Since prices are in $/bbl we obtain €/l prices by application of our smoothed exchange rate factor.
11.3. Gasoline and diesel price in MMEM
The ADAC reports long t ime series about the historical prices of the fuels gasoline (Normal and Super) and diesel . Notably average fuel prices in 2010 are reported to be 1,405 €/liter for gasoline (Super) and 1,214 €/liter for Diesel (ADAC 2011). The following table shows an extraction of the ADAC time series about annual average gasoline and diesel service station prices: &
Year Gasoline (Normal)
Gasoline (Super)
Diesel
2000 193.9 198.8 156.9
2001 195.9 200.0 160.3
2002 102.5 104.6 83.6
2003 107.1 109.2 88.4
2004 111.3 113.2 93.7
2005 119.7 121.7 106.1
151
2006 126.0 128.0 110.9
2007 132.2 133.7 116.0
2008 138.9 138.9 132.4
2009 127.2 127.3 107.7
2010 - 140.5 121.4
Table 122: Annual average fuel prices, Source: (ADAC 2011)
For a comparison we display numbers about ARAL service station fuel prices for 2005 to 2011. ARAL already provides
intermediate values for 2011 and levies as percentages of fuel prices:
&
Year Gasoline price (€ct/l)
Diesel price (€ct/l)
Gasoline levies (%)
Diesel levies (%)
2005 120.3 106.8 68.8 58.4
2006 126.7 111.7 65.9 56.3
2007 133.0 117.1 65.6 56.6
2008 139.5 133.5 63.5 51.8
2009 128.0 108.8 67.5 59.6
2010 141.3 122.7 62.6 54.6
*2011 153.8 141.3 58.8 49.5
*call date: 31. August 2011
Table 123: ARAL gasoline and diesel service station prices and levies, Source: (ARAL 2011)
Additionally, the (ADAC) reports about most recent fuel prices per l i ter : &
Fuel price
Normalbenzin 1,46 €/l
Super 1,46 €/l
SuperPlus 1,53 €/l
Diesel 1,30 €/l
Bio-Ethanol 1,05 €/l
Autogas (LPG) 0,75 €/l
Erdgas (CNG) 0,95 €/kg
Table&124:&Most&recent&fuel&prices&reported&by&(ADAC)&
In our scenarios we model the development of future fuel prices in dependence of future oi l prices. In our model gasoline and diesel prices are calculated as fol lows: Gasoline or diesel price (€/l) = (oi l price (€/l) + addit ional fuel costs (€/l) + fuel tax (€/l)) × VAT
(36)!
152
We assume additional fuel costs of 18 €ct for gasoline and 20 €ct for diesel which are time-invariant. These values
were calibrated manually in order to replicate 2010 yearly average pump prices in function of the 2010 yearly average
international oil price. Data were obtained from the (Statistisches Bundesamt, Durchschnittsdaten für 2011).
Instead of using an additive term we also considered to apply a multiplicative term for calculating fuel prices from
international oil prices. So the additional charge amounts 22,6 % of the oil price for gasoline and 25,7 % for diesel in
2010.
11.4. Biofuel E85 production costs and prices
It can be assumed that bioethanol prices are related to bioethanol production costs and are linked to the oil price.
The production costs of bioethanol vary across countries, ethanol resources and time:
! (Wikipedia) reports ethanol production costs of 16 €ct/l from sugarcane in Brazil, 26 €ct/l for sweet
corn ethanol in the USA and 45 €ct/l ethanol from sugar beets in the EU.
! The (Bayerische Landesanstalt für Landwirtschaft 2006) reports about the bioethanol price
development:
Die Preisentwicklung für Ethanol ist auf allen Märkten ansteigend, wobei der Weltmarktpreis ebenfalls steigend ist und Ende 2005 bei 0,26 €/l lag. US-Ethanol kostet gut 0,10 €/l mehr. Für Europa beträgt der Außenschutz für Ethanol 0,192 €/l.
In den vergangenen 12 Monaten ist in Deutschland der Ethanolpreis innerhalb kurzer Zeit von knapp unter 0,50 €/l auf über 0,65 €/l angestiegen. Damit zeigt sich eine deutliche Abhängigkeit zum Rohölpreis. […] Kurzfristig könnten, unterstützt durch internationale Krisen und Unsicherheiten, die Preise auch im Sog weiter steigender Rohölpreise ansteigen. Der Ausbau der Verarbeitungskapazitäten dürfte jedoch die Entwicklung nach oben begrenzen.
(Bayerische Landesanstalt für Landwirtschaft 2006)
Concerning bioethanol E85 the ADAC reports:
Da Ethanol einen um rund 35 % geringeren Energiegehalt als Ottokraftstoff hat, ist mit einem Mehrverbrauch von bis zu einem Drittel zu rechnen. Der Preis für E85 an den Tankstellen differiert regional stark, liegt aber durchschnittlich um ungefähr 20 Cent unter dem Niveau für herkömmliches Superbenzin. Der günstige Preis ergibt sich aus der Bevorzugung bei der Mineralölsteuer. Bioethanol ist bis Ende 2009 von der Energiesteuer befreit. Für Kraftstoffe mit einem Bioethanolanteil von 70 bis 90 % gilt die Steuerbefreiung für den entsprechenden Bioethanolanteil sogar bis Ende 2015.
(ADAC)
Some internet enquiries about the price development of E85 reveal that service stations are to some extent arbitrary in the pricing of this fuel . Price variat ions to the extent of 0.30 € are reported for different service stations in a region and also price increases up to 0.20 € on a service station in one day: Der aktuel le Preis für E85 schwankt ähnlich wie der von Benzin und weist innerhalb Deutschlands gewisse regionale Unterschiede auf. Der günstigste Anbieter verkauft heute einen Liter für 0,90€ an den Endverbraucher, der teuerste verlangt 1,29€ . Im Durchschnitt musste der Autofahrer am 13.2.2008 0,97€ pro Liter E85 bezahlen (Stand 13.2.2008) [ethanol-tanken.com, online, 2008]. (Brist le 2008), p. 34
From this statement it can be concluded that the bioethanol price might be high volati le even if the production costs of bioethanol seem to be somewhat stable. High price variations for E85 are possibly due to temporary stock-out of this fuel .
153
The following table shows calculations of E85 prices in €/l for the (ADAC 2011) Gasoline (Super) 2010 price and
different numbers of bioethanol production costs:
&
E85 components €/liter price scenarios E85 component
weights
Ethanol production costs in €/l 0.5500 0.6500 0.7500
Retail remuneration (diesel) 0.0773 0.0773 0.0773
Ethanol retail price incl. VAT in €/l 0.7465 0.8655 0.9845 0.85
Gasoline price incl. taxes in €/l 1.4050 1.4050 1.4050 0.15
Calc. bioethanol (E85) price incl. taxes 0.8453 0.9464 1.0476
Table& 125:& Calculated& E85& consumer& prices& for& the&ADAC& gasoline& 2010& price& and& different& scenarios& for& ethanol&
production&costs&
&
E85 components €/liter price scenarios E85 component
weights
Ethanol production costs in €/l 0.55 0.65 0.75
Retail remuneration (diesel) 0.0773 0.0773 0.0773
Ethanol retail price incl. VAT in €/l 0.7465 0.8655 0.9845 0.85
Gasoline price incl. taxes in €/l 1.538 1.538 1.538 0.15
Calc. bioethanol (E85) price incl. taxes 0.8652 0.9664 1.0675
Table&126:&Calculated&E85&consumer&prices&for&the&ARAL&preliminary&2011&gasoline&price&and&different&scenarios&for&
ethanol&production&costs&
We found the following information about historical and recent E 85 prices:
! (C.A.R.M.E.N. 2011) reports for bioethanol E 85 a price of 0,95 €/l in October 2007, 0,981 €/l in
November 2010 and 1,063 €/l in July 2011. Additionally, (C.A.R.M.E.N. 2011) displays a service station
price index for E 85 prices. According to this price index the E 85 price moved in a range between 0,90
€/l and 1,10 €/l from July 2009 to July 2011. The index chart can be found in the appendix.
! The (Bundesverband der deutschen Bioethanolwirtschaft e.V.) provides a chart of monthly bioethanol
prices (€/m3) for 2009 in Rotterdam and the territory of Germany and the Benelux countries. This chart
can also be found in the appendix.
We assume that bioethanol is produced in Germany for 55 €cent/liter in 2006/2007 (ex-clusive taxes?). Further, we believe that bioethanol production costs are nearly constant over t ime while the gasoline price component in the E85-fuel is more volati le. A lower limit of the E 85 price is set by the minimum of the costs for production, transport etc. of bioethanol
produced in the EU and the international market price additional to the EU external protection.
An upper limit of the E 85 price is given by the price of the competitive fuel gasoline. Considering that people use the
so-called “flexifuel” technology, they would not fill their cars with bio ethanol if it is cheaper to use a conventional
fuel (taking into account the larger consumption of bioethanol in liter/km compared to gasoline).
154
We assume that bioethanol is produced in Germany for 55 €cent/liter in 2006/2007. Further, we assume that
bioethanol production costs are nearly constant over time while the gasoline price component in the E85-fuel is more
volatile.
The biofuel (E85) price is calculated as: E!85!price!(€/l)!=!0,15!×!gasoline!price!+!0,85!×!VAT!×!(current!prod.!costs!of!bioethanol!+!gross!margin!diesel)!×!oil!price!index!
(37)!
11.5. Liquefied Petroleum Gas (LPG) price
It can be assumed that the LPG price movement is highly correlated to the oil price movement, since LGP is a by-
product in the production process of gasoline.
ARAL explains:
Flüssiggas wird vorwiegend bei der Förderung von Erdgas und Rohöl gewonnen. Ferner entsteht es bei der Destillierung von Rohöl. Das in Deutschland eingesetzte Flüssiggas stammt aus den deutschen Raffinerien und der Nordseeförderung.
(ARAL 2011)
Historical index values of the LPG price are reported by the German Federal Statistical Office (Statistisches
Bundesamt, Fachserie 17, Reihe 2, 11/2010) „Index der Erzeugerpreise gewerblicher Produkte (Inlandsabsatz)“, index
values of the annual average price (before VAT) for „Flüssiggas (LPG), als Kraft- oder Brennstoff“:
&
Year Index value
2000 78.9
2001 70.2
2002 63.3
2003 71.1
2004 83.5
2005 100.0
2006 115.1
2007 116.2
2008 134.7
2009 103.8
2010
* Index value of base year 2005 is set to 100
Table 127: Index of industrial producer prices of industrial products (domestic sales), liquefied petroleum gas (LPG), used as petrol or combustible, Source: DESTATIS
(Wikipedia 2011) reports an average LPG or autogas price of 75 €ct/l which we take as a start value for 2011.
Concerning the future scenario for the gas price movement we rely on the EIA gas market price forecast in $/btu
which we have on hand as an annual time series for 1.1.2010 to 1.1.2051. The EIA 2010 gas market price amounts 4,5
$/btu. We calculate index values from the EIA gas market price forecast setting the first value to 1. We assume an lpg
price of 0,7 €/l (besser 0,55, s.) in 2010 and believe that subsequent values will develop according to the index.
155
11.6. Hydrogen price
We consider information about hydrogen prices from different sources:
! (The Connecticut Center for Advanced Technology Inc. 2011) reports about hydrogen:
One of the defining features and benefits of hydrogen for use as a transportation fuel is the ability for it to be produced from a number of different sources, including renewable energy, fossil fuels, and nuclear power. The wide range of hydrogen sources means that a hydrogen economy is not dependent on any single resource for energy. The cost of the hydrogen produced depends on the type and size of the dispensing station as well as the source of the hydrogen. Steam methane reformed (SMR) hydrogen has prices per kg ranging from $2.19 to $3.51 per kg. Hydrogen from renewable energy currently ranges in prices from $1.30 per kg for hydrogen produced from large hydroelectric turbines to over $40 per kg for hydrogen produced from the most expensive photovoltaic (PV) power systems. Currently, CTTransit pays approximately $4.50/kg for liquid hydrogen trucked from a generating plant near Niagara Falls where the hydrogen is produced by hydro-powered electrolysis units.
(The Connecticut Center for Advanced Technology Inc. 2011), p. 42
! (Linnemann 2005) calculated for a pilot plant production costs of 69,7 €cent/kWh hydrogen and for a
large scale plant production costs of 29 €cent/kWh hydrogen. Taking into account the lower heating
value 33,33 kWh/kg hydrogen, consumer prices incl. VAT of 11,39 to 27,37 €/kg hydrogen are
calculated.
! (Tetzlaff 2008), p. 207 claims that hydrogen is a competitive product to natural gas at his market
launch. So the hydrogen price could be linked to the natural gas price. If the hydrogen price is linked to
the natural gas price and the CNG price inclusive taxes amounts 7,14 €cent/kWh (useful heat) then the
hydrogen price amounts at a maximum 2,38 €/kg hydrogen equal to 7,14 €cent/kWh times 33,33
kWh/kg.
! (TCS) reports:
14 Liter flüssiger Wasserstoff in einen Eimer, würden lediglich 1 kg wiegen. […] Derzeit kostet Wasserstoff in Deutschland ca. 8 €/kg, das sind umgerechnet CHF 12.–/kg, ohne Mineralölsteuer und Ökosteuer. Der Energiegehalt von 1 kg Wasserstoff entspricht 3.7 l Benzin. So gesehen entspricht dieser Preis für Wasserstoff bezogen auf den Energiegehalt einem Benzinpreis von etwa CHF 3.25 [2,16€] pro Liter [Benzin].
(TCS), p. 1
! (Geitmann) reports
Die Bestimmung des aktuellen Preises von Wasserstoff ist ein sehr schwieriges Unterfangen, weil er je nach Herstellungsverfahren sehr stark variiert. Dabei spielt sowohl die Produktionsmethode als auch der Weg der Energie- Erzeugung eine wesentliche Rolle. Momentan liegt der Liter-Preis für Wasserstoff je nach Herstellungsverfahren durchschnittlich bei 0,50 Euro, was knapp 2,- Euro für einen Liter Benzin entsprechen würde. Als Vergleichsgrundlage wird hierbei das so genannte Benzin-Äquivalent herangezogen. Für die Umrechnung wird die vorgegebene Energiemenge an Wasserstoff mit der gleichen Energiemenge von Benzin gleichgesetzt. Man erhält dann als Ergebnis, dass ein Liter Benzin etwa die vierfache Menge Wasserstoff entspricht.
(Geitmann)
It is not really clear if the claim of (Geitmann) relates to production costs or purchase prices of
hydrogen. We assume that he relates to production costs. So we take from (Geitmann) a price incl. VAT
of about 0,60 €/liter hydrogen. Taking into account the TCS stated ratio of 14 l/kg hydrogen this price
corresponds to 8,40 €/kg hydrogen.
156
! On January 5th, 2011 a “Total” service station in Berlin demands an up-to-date price of 8,66 €/kg
hydrogen.18 Although this price could be rather a fictive price linked to test driving than an actual
market price it confirms the price values reported by (Geitmann) and (TCS).
Concerning the development of the hydrogen price
! (TCS) reports:
Spezialisten rechnen damit, dass bei großer Nachfrage mittelfristig etwa eine Halbierung dieses Preises erreicht werden kann. Ob dabei berücksichtigt wurde, dass bei großer Wasserstoff-Nachfrage auch der Preis für den Rohstoff Erdgas steigt, ist zu bezweifeln.
(TCS)
! (The Connecticut Center for Advanced Technology Inc. 2011), p. 28 assumes a hydrogen price/gallon
equivalent in USD of 4,67 USD in 2010, 3,91 USD in 2020, 3.65 USD in 2035 and 3,58 USD in 2050.
Conclusion: According to (TCS) we assume a price before VAT of 0,5 €/l or 7 €/kg hydrogen. We assume that the
hydrogen price declines according to the ratios of US$ fuel prices reported in (The Connecticut Center for Advanced
Technology Inc. 2011), p. 28 by 3,91$/4,67$ in 2020, 3,65$/4,67$ in 2035 and 3,58$/4,67$ in 2050.
&
Established in time Value
1.1.2011 1
1.1.2020 0,8373
1.1.2035 0,7816
1.1.2050 0,7665
Table&128:&Hydrogen&cost°ression&scenario&according&to&(The&Connecticut&Center&for&Advanced&Technology&Inc.&
2011)&
The hydrogen price is calculated as: hydrogen! price! (€/kg)! =! 7! (€/kg)! ×! VAT! ×! The! Connecticut! Center! cost!degression!scenario!
(38)!
18 Total Tankstelle, 13593 Berlin Spandau, Heerstrasse 324, phone: +49 30 30124300.
157
12. Appendix
Further information on car attributes
12.1. Best seller Gasoline and Diesel cars according to KBA/ADAC
The values for the vehicle attr ibutes purchase price, fuel consumption, horse power, range, CO2-emissions and curb weight base on the ADAC search engine, requested in Dec. 2010, for the fol lowing car models : Conclusions about the average attribute values of gasoline and diesel cars in the KBA segments are drawn from these
gasoline and diesel cars which are best sellers in each segment:
Table 1 Best seller gasoline cars, Source: ADAC search engine request in Dec. 2010
Segment according to KBA
Gasoline vehicle
Mini
Renault Twingo 1.2 60 Authentique
smart fortwo coupe 1.0 mhd pure softip
Kleinwagen
VW Polo 1.2 Trendline
Opel Corsa 1.2 Twinport ecoFlex Selection
Kompaktklasse
VW Golf 1.4 Trendline
Opel Astra 1.4 ecoFlex Selection
Mittelklasse
BMW 318i
VW Passat 1.6 Trendline (bis 04/10)
Audi A4 1.8 TFSI Attraction
Mercedes C 180 Kompressor BlueEFFICIENCY Classic (bis 04/10)
Obere Mittelklasse
Mercedes E 200 CGI BlueEFFICIENCY
BMW 523i
Audi A6 2.0 TFSI
Oberklasse
Mercedes S 350 7G-Tronic Sport
BMW 740i Automatic
Geländewagen
Toyota RAV4 2.0 4x2
Mercedes ML 350 4Matic (7G-Tronic)
Sportwagen Mercedes SLK 200 Kompressor
Mini-Van
Opel Meriva 1.4 Selection
Renault Scenic 1.6 16V 110 Expression
Großraum-Van
Opel Zafira 1.6 ecoFlex Selection (bis 06/10)
VW Touran 1.2 TSI Trendline (ab 08/10)
Utility VW Caddy Kastenwagen 1.4 Economy
158
Table 2 Best seller diesel cars, Source: ADAC search engine requested in Dec. 2010
Segment according to KBA
Diesel vehicle
Mini Renault Twingo dCi 65 Eco2
smart fortwo coupe 0.8 cdi pure softip (DPF) (ab 09/10)
Kleinwagen VW Polo 1.2 TDI BlueMotion 87g (DPF)
Opel Corsa 1.3 CDTI ecoFlex Selection (DPF) (bis 06/10)
Kompaktklasse VW Golf 1.6 TDI Trendline (DPF)
Opel Astra GTC 1.3 CDTI Selection (DPF)
Mittelklasse
BMW 316d (DPF)
VW Passat 1.6 TDI BlueMotion Technology Trendline (DPF)
Audi A4 2.0 TDI Attraction (DPF)
Mercedes C 200 CDI BlueEFFICIENCY Classic (DPF)
Obere Mittelklasse
Mercedes E 200 CDI BlueEFFICIENCY (DPF)
BMW 520d touring Special Edition (DPF) (bis 07/10)
Audi A6 2.0 TDI e (DPF)
Oberklasse Mercedes S 350 CDI BlueEFFICIENCY 7G-Tronic (DPF) (bis 07/10)
BMW 730d Automatic (DPF)
Geländewagen Toyota RAV4 2.2 D-4D 4x2 (DPF)
Mercedes ML 300 CDI BlueEFFICIENCY 4Matic (7G-Tronic) (DPF)
Sportwagen -
Mini-Van Opel Meriva 1.3 CDTI ecoFlex Selection (DPF)
Renault Scenic dCi 110 FAP Expression
Großraum-Van Opel Zafira 1.7 CDTI Selection (DPF) (bis 06/10)
VW Touran 1.6 TDI Trendline (DPF) (ab 08/10)
Utility VW Caddy Kastenwagen 2.0 SDI Economy
12.2. Manufacturers and consumers statements about fuel consumptions of gasoline and diesel vehicles
Table 3 Manufacturers and consumers statements about fuel consumptions of gasoline vehicles, Source: Manufacturers‘ statements from ADAC (call date: Dec. 2010) and consumers‘ statements from Spritmonitor.de (call date 10. August 2011)
Gasoline vehicles Statements about fuel consumption in liters
Segmentation "Best seller" Manufacturers Consumers Deviation (%)
Mini Renault Twingo 5.5 6.2 12.0
Kleinwagen Opel Corsa 1.2 5.3 7.1 34.0
Kompakt Golf V 1.4 Trendline 6.4 6.9 7.7
Mittelklasse BMW 318i 6.3 8.0 26.8
159
Obere Mittelklasse BMW 523i 7.6 9.4 23.3
Oberklasse Mercedes S350 7G 10.0 11.7 17.2
Geländewagen Toyota RAV 7.4 9.1 23.0
Sportwagen Mercedes SLK 200 Kompressor 7.7 8.1 4.7
Mini-Van Opel Meriva 1.4 6.1 6.7 10.0
Großraum-Van Opel Zafira 1.6 7.1 8.3 17.5
Utility VW Caddy 7.9 8.6 8.4
average weighted with percentage of new registrations in each segment 18.6
Table 4 Manufacturers and consumers statements about fuel consumptions of diesel vehicles, Source: Manufacturers‘ statements from ADAC (call date: Dec. 2010) and consumers‘ statements from Spritmonitor.de (call date 10. August 2011)
Diesel vehicles Statements about fuel consumption in liters
Segmentation "Best seller" Manufacturers Consumers Deviation (%)
Mini Renault Twingo 4.3 4.9 14.9
Kleinwagen Opel Corsa 1.3 CDTI 4.3 6.0 40.5
Kompakt VW Golf 1.6 TDI 4.5 6.5 44.4
Mittelklasse BMW 316d 4.5 6.0 33.1
Obere Mittelklasse BMW 520d 5.3 7.0 32.6
Oberklasse Mercedes S 350 CDI BlueEfficiency 7.6 9.7 28.0
Geländewagen Toyota RAV 6.0 7.5 24.8
Sportwagen
Mini-Van Opel Meriva 1.3 CDTi 4.5 5.3 17.6
Großraum-Van Opel Zafira 1.7 5.7 6.5 14.7
Utility VW Caddy 6.0 7.3 21.0
average weighted with percentage of new registrations in each segment 32.9
12.2.1. Statments from other sources about car prices
We found information about non-battery excess costs or prices of vehicles with alternative propulsion technologies or
fuel consumptions from several sources:
! The (International-Energy-Agency) reports on its website:
The cost of HEVs [Hybrids in our terms] is about US$ 5000 [4250 €] more than comparable conventional vehicles, and their fuel economy is about twice that of their conventional counterparts
and
The extra cost of the car, currently about US$5 000 [4250€], can be substantially reduced as manufacturing experience is acquired for the batteries, which currently cost about US$3000 [2550€]
(International-Energy-Agency)
160
So 1700 € extra costs exclusive of battery costs are calculated from the (International-Energy-Agency) information
source.
! (National Research Council 2010) estimates non-battery extra costs in USD of PHEV (in our terms) and RE
(in our terms), which are converted in euro about 2.550 € and 3.400 €, respectively.19
While these costs could probably be refined they offer a fair ly sufficient approximation. For simplif ication purpose we suppose that these extra costs are identical across segments.
! (Brooker, Thornton et al. 2010) estimate component costs based on previous studies published in 2006:20 The conventional vehicle costs are used to estimate the HEV, PHEV, and EV costs. The engine cost is subtracted from the conventional vehicle price. Then the advanced vehicle component costs are added. This approach matched closely for a range of advanced vehicles with different component sizes.
(Brooker, Thornton et al. 2010), p. 3
Table& 5& (Brooker,& Thornton& et& al.& 2010):& Component&manufacturing& cost& and&markup& factor& applied& to& calculate&
price&to&consumer&
Battery 22 $/kW + 700 $/kWh + 680 $
Motor and controller 21.7 $/kW + 425 $
Engine 14.5 $/kW + 531$
Markup factor 1.75
! (Moawad, Sharer et al. 2009) &
Table&6&(Moawad,&Sharer&et&al.&2009):&Input¶meter&and&assumptions&for&cost&analysis&
Parameter Value
Engine 300 + 3 × Power + 275 × Cylinder number
HEV (Hybrid in our terms) battery 40 $/kW
PHEV21 battery 380 × Total energy + 25 × Peak power
Electric machine (EM) 7 $/kW
19 NRC ! ( 2 010 ) , ! p . ! 4 ! r e po r t : “Costs to a vehicle manufacturer for a PHEV-40 built in 2010 are l ikely to be about $14,000 to $18,000 more than an equivalent conventional vehicle, including a $10,000 to $14,000 battery pack. The incremental cost of a PHEV-10 would be about $5,500 to $6,300, including a $2,500 to $3,300 battery pack.“ Taking!an!exchange!rate!of!0,85!€/$!it!is!calculated!that!the!nonEbattery!costs!increase!to!2.550!€!(equal!to!3.000!US$!=!5.500!US$!E!2.500!US$)!for!the!PHEVE10!vehicle!(the!PHEV!equivalent!of!our!study)!and!to!3.400!€!(equal!to!4.000!US$!=!14.000!US$!E!10.000!US$)!for!the!PHEVE40!vehicle!(the!RE!equivalent!of!our!study)!compared!to!a!conventional!vehicle!in!the!same!segment.!NRC!(2010),!p.!14!give!an!overview!of!the!costs!of!vehicle!components!of!PlugEin!Hybrid!Electric!Vehicles,!Range!Extenders!(as!in!our!definition)!and!conventional!vehicles.!20!Simpson,!A.!(2006).!CostEBenefit!Analysis!of!PlugEIn!Hybrid!Electric!Vehicle!Technology.!22nd!International!Battery,!Hybrid!and!Fuel!Cell!Electric!Vehicle!Symposium!and!Exhibition!(EVSE22).!Yokohama,!Japan.!U.S. Energy Information Administration, www.eia.doe.gov, accessed July 10, 2006. 21!PHEV!and!RE!in!our!terms.!
161
EM controller 9 $/kW
Battery charger $ 800
From these formulas (Moawad, Sharer et al. 2009) calculate the following cost numbers:
Table&7&Estimated&vehicle&costs&by&(Moawad,&Sharer&et&al.&2009)&from&Table&6&
Parameter Vehicle cost ($)
Conventional 17245
HEV 20029
PHEV 4 kWh 21881
PHEV 8 kWh 23709
PHEV 12 kWh 27487
PHEV 16 kWh 29338
! Argonne National Laboratories
Table 8 Manufacturing costs (€) of vehicle components, Source: Own calculations from data published in Ferchau (2010): “PEV Cost Trends and Benefits of Advanced Transmission Technologies” and based on Argonne National Laboratories
&
Component System Conventional vehicle PHEV Series
PHEV Parallel
BEV
Body 3620 3609 3607 3613
Engine 2945 2921 2941 2949
Transmission 688 687 687 664
Chassis 3549 3535 3543 3533
Vehicle Assembly 2878 2872 2877 2869
Motors/Gens/high voltage 3412 1675 2603
High power electronics 1424 730 1116
Batteries (16P/24B) 6088 5389 9139
additional non battery costs in € for electrification
4836 2405 3719
Cost in € 13680 24548 21449 26486
Comment: PHEV series are RE vehicles in our terms while PHEV parallel are PHEV.
It is doubted that a BEV has the same ICE size compared to a gasoline vehicle.
12.2.2. Car price development
! Krail
Table&9&(Krail&2008)&assumptions&about&car&price&developments&of&several&technologies&in&2020,&2030&and&2040&for&
the&EU&27+2&
162
Car technology Car price development
2020 2030 2040
Conventional 25% 38% 41%
CNG 13% 17% 19%
Hybrid 13% 17% 19%
Electric 13% 17% 19%
Bioethanol 3% 0% -2%
Hydrogen 0% -13% -26%
12.2.3. Calculation of excess car prices from data provided by Blesl
! (Blesl M., Bruchof D. et al. 2009), p. 33
Table 10 Anteile einzelner Komponenten an der Gesamt-Investitionskosten der verschiedenen Antriebsalternativen – Kleinstwagen, Source: Blesl
Kleinstwagen heute Einheit Referenz- fahrzeug BEV FCEV FCHEV HEV (Full)
HEV (Mild) PHEV
Karosserie EUR 8000 8000 8000 8000 8000 8000 8000
ICE EUR 1350 0 0 0 1350 1350 810
Getriebe EUR 0 0 0 0 0 0 0
Benzin-/Dieseltank EUR 125 0 0 0 125 125 125
H2-Speicher EUR 0 0 555 505 0 0 0
E-Motor EUR 0 833 833 833 555 167 833
E-Motorsteuerung EUR 0 1020 1020 1020 1020 1020 1020
Batterie EUR 0 6420 0 1000 1000 600 3840
Brennstoffzelle EUR 0 0 16650 16650 0 0 0
DC/DC-Wandler EUR 0 300 300 300 300 300 300
AC/DC-Wandler EUR 0 410 0 0 0 0 410
Gesamtkosten EUR 9475 16983 27358 28307 12350 11562 15338
Table 11 Anteile einzelner Komponenten an der Gesamt-Investitionskosten der verschiedenen Antriebsalternativen – Mittelklassewagen, Source: Blesl
Mittelklassewagen heute Einheit Referenz- fahrzeug BEV FCEV FCHEV HEV (Full)
HEV (Mild) PHEV
Karosserie EUR 16165 16165 16165 16165 16165 16165 16165
ICE EUR 3930 0 0 0 2250 2250 1350
Getriebe EUR 0 0 0 0 0 0 0
Benzin-/Dieseltank EUR 125 0 0 0 125 125 125
H2-Speicher EUR 0 0 1688 1535 0 0 0
E-Motor EUR 0 1388 1388 1388 925 278 1388
E-Motorsteuerung EUR 0 1020 1020 1020 1020 1020 1020
Batterie EUR 0 21840 0 1300 1300 900 7836
163
Brennstoffzelle EUR 0 0 27750 27750 0 0 0
DC/DC-Wandler EUR 0 300 300 300 300 300 300
AC/DC-Wandler EUR 0 410 0 0 0 0 410
Gesamtkosten EUR 20220 41123 48311 49457 22085 21038 28594
Table 12 Anteile einzelner Komponenten an der Gesamt-Fahrzeugmasse bei den verschiedenen Antriebsalternativen– Kleinstwagen, Source: Blesl
Kleinstwagen heute Einheit Referenzfahrzeug BEV FCEV FCHEV HEV (Full) HEV (Mild) PHEV
Karosserie kg 636 636 636 636 636 636 636
ICE kg 99 0 0 0 99 99 60
Getriebe kg 0 0 0 0 0 0 0
Benzin-/Dieseltank kg 15 0 0 0 15 15 15
H2-Speicher kg 0 0 32 29 0 0 0
E-Motor kg 0 45 32 32 21 6 32
E-Motorsteuerung kg 0 14 14 14 14 14 14
Batterie kg 0 76 0 14 14 9 46
Brennstoffzelle kg 0 0 96 96 0 0 0
DC/DC-Wandler kg 0 2 2 2 2 2 2
AC/DC-Wandler kg 0 2 0 0 0 0 2
Gesamtmasse kg 750 775 811 823 802 781 806
Table 13 Anteile einzelner Komponenten an der Gesamt-Fahrzeugmasse bei den verschiedenen Antriebsalternativen– Mittelklassewagen, Source: Blesl
Mittelklassewagen heute Einheit Referenzfahrzeug BEV FCEV FCHEV HEV (Full) HEV (Mild) PHEV
Karosserie kg 1038 1038 1038 1038 1038 1038 1038
ICE kg 198 0 0 0 166 166 99
Getriebe kg 0 0 0 0 0 0 0
Benzin-/Dieseltank kg 15 0 0 0 15 15 15
H2-Speicher kg 0 0 97 89 0 0 0
E-Motor kg 0 75 53 53 36 11 54
E-Motorsteuerung kg 0 14 14 14 14 14 14
Batterie kg 0 260 0 19 19 13 93
Brennstoffzelle kg 0 0 160 160 0 0 0
DC/DC-Wandler kg 0 2 2 2 2 2 2
AC/DC-Wandler kg 0 2 0 0 0 0 2
Gesamtmasse kg 1251 1391 1364 1374 1289 1258 1317
164
Table& 14& Segment& independent& and& segment& dependent& manufacturing& costs& for& cars& of& different& technologies,&
calculated&from&Blesl&&
technologies/ segments
Reference vehicle
BEV FCEV FCHEV HEV (Full)
HEV (Mild)
PHEV curb weight (kg) of best sellers (ADAC)
segment independent costs:
125 1730 1320 1320 1445 1445 1855
segment dependent costs:
Mini 1350 833 18038 17988 1905 1517 1643 913
Kleinwagen 2235 1023 22427 22341 2341 1864 2019 1099
Kompaktklasse 3168 1224 27051 26928 2800 2230 2415 1295
Mittelklasse 3930 1388 30826 30673 3175 2528 2738 1455
Obere Mittelklasse 4796 1574 35120 34933 3601 2867 3106 1637
Oberklasse 6120 1859 41679 41439 4253 3386 3667 1915
Geländewagen 5658 1760 39391 39169 4026 3205 3471 1818
Sportwagen 3621 1321 29292 29152 3023 2407 2607 1390
Mini-Van 3563 1309 29009 28871 2995 2384 2582 1378
Großraum-Van 4044 1413 31392 31235 3231 2573 2786 1479
Utility 3763 1352 30000 29854 3093 2463 2667 1420
12.2.4. Extra purchase prices of Bioethanol vehicles
We consider the following information sources:
! The ADAC reports:
Es kann somit jede beliebige Ethanol-Ottokraftstoff-Mischung bis zu einem max. Anteil von 85 % Ethanol (E85) aber auch ausschließlich Ottokraftstoff getankt werden. Die Mehrpreise gegenüber den entsprechenden Benzin-Versionen liegt bei bis zu 1.500 € je nach Hersteller/Modell.
(ADAC Bioethanol)
! In another source it is reported:
Die Mehrkosten in der Herstellung von FFV-Fahrzeugen belaufen sich auf ca. 300-500 Euro. Allerdings sind die Hersteller bestrebt, die Preise an das Benzin-Basismodell anzupassen, abhängig vom Absatzvolumen solcher Fahrzeuge.
(http://www.e85-fahren.ch/faq.php)
! In an internet discussion forum it is claimed:
Diese Umrüstung ist relativ preisgünstig und beträgt ab Werk kaum 300 Euro für ein anderes oder weiteres Steuergerät. Da GM viele Modelle ab Werk ohne Aufpreis für E85-Betrieb anbietet, stellt sich hier die Frage für die Extrakosten. Schließlich gehört Saab/Opel zu GM und Volvo zu Ford.
Höhere Kosten können nur entstehen, wenn Teile der Einspritzung und Kraftstoffversorgung nicht korrosionsbeständig sind. Das ist bei den neueren Modellen sicherlich nicht mehr der Fall.
(Xing group Alternative Energies and Sustainability 2006)
We decided to rely on information from model-wise comparisons.
165
12.3. Energy content and power of batteries
This section gives an overview about energy density numbers for battery technologies reported in different studies. It
can be assumed that the energy content increases by time by improvement of battery technologies.
! (Martinet 2004) reports energy contents for different battery technologies as follows:
Table 15 Energy density in Wh/kg for different battery technologies, Source: (Martinet 2004)
Plomb 30
Nickel Cadmium 30 - 50
Nickel Métal Hydrure 70 - 80
Lithium Ion 150 - 170
! (Lowe, Tokuoka et al. 2010), p. 13 report for Li-ion batteries an energy density of 120 Wh/kg which is
decisive for range and a power density of 1800 W/kg which is decisive for acceleration.
! (Wansart and Schnieder 2010), p. 4 report a battery energy density of 0.12 kWh/kg and a BEV energy
consumption of 0.39 kWh/mi.
! (Nemry and Leduc 2009), p. 13 report energy densities of 46 Wh/kg for NiMH and 110 to 160 Wh/kg for
Li-ion batteries. For NiMH batteries used for the Toyota Prius III (Nemry and Leduc 2009) report a total
capacity of 1,3 kWh and a weight of 29 kg. This equates to an energy density of about 45 Wh/kg.
! (U.S. Department of Energy 2007), p. 20 reports for Li-ion batteries a current (2007) capability of about
70 Wh/kg and near and long-term goals of 100 Wh/kg and 150 Wh/kg.
! (Deutsche Bank 2009) estimates prices of high-energy batteries for electric vehicles. The prices include
costs of cells, packaging, and the battery management system as well as warranty cost and 30% gross
margin. The price in USD/kWh is based on 25% de4cline by 2015 and 50% by 2020.
Table&16&Deutsche&Bank&estimates&for&pricing&in&USD&to&the&OEM&of&highIenergy&EVIbatteries&
Year 2010 2015 2020
Price/kWh 650 488 325
kWh/battery 25 25 25
Total battery system
16250 12188 8125
12.3.1. Nameplate and available capacity
Regarding this difference the following information sources were considered:
166
! (National Research Council 2010) reports that only 50 percent of the nameplate energy of a battery is
usable to ensure battery durability and safety.22
! (Wikipedia: Chevrolet Volt) reports that the Volt’s Li-ion battery pack stores 16 kWh of energy “but it is
controlled or buffered via the energy management system to use only 10.4 kWh of this capacity to
maximise the life of the pack. For this reason the battery pack never fully charges or depletes, as the
software only allows the battery to operate within a state of charge (SOC) window of 65% […]”
! (Wikipedia: Chevrolet Volt (Dt.)) reports for the Chevrolet Volt a state of charge between 30 and 85
percent.23 So the usable energy (or state-of-charge range) amounts to 55 percent of the nameplate
energy for the Chevrolet Volt.
! (Thomas 2010), p. 35 reports that “the […] Chevy Volt […] with 40 miles all-electric range reportedly
has a 181-kg battery with a nameplate energy of 16 kWh. But only half of this energy can actually be
used, so the useful specific energy is 0.44 Wh/kg […]. Similary, according to the literature, the Audi ‘e-
tron’ 471-kg battery pack has a nameplate energy rating of 53 kWh, of which 80% can be used to propel
the car, resulting in a useful specific energy of 0,0899 kWh/kg […]. Finally, the US Department of
Energy estimates that the current useful specific energy of Li-ion battery systems is approximately
0.025 kWh/kg […].”
! (Rousseau, Shidore et al. 2007), p. NN, section 3 assume a SOC between 29% and 89%.24
! (Rousseau, Shidore et al.), [p. 2] assume a 60% range of the SOC between 0,3 and 0,9.25
! (U.S. Department of Energy 2007), p. 17 – 18 assume for
o BEV a charge depleting (CD) between 20% and 100% or a SOC range of 80% (=20% unused
capacity) for about 1000 deep discharge cycles, more than 40 kWh and a power-to-energy ratio
(P/E) equal to 2;
o PHEV (PHEV or RE in EMOB terms) a charge depleting and sustaining between 20% and 100% or a
SOC range of 80% (=20% unused capacity) for up to 5000 deep cycles, about 5 to 15 kWh and a
P/E between 3 to 15.
o HEV (Hybrid in EMOB terms) a charge sustaining (CS) only of about less than 60% to about 70%
or a SOC range of about 10% (about less than 60% unused capacity and 30% uncharged capacity)
for about 300.000 shallow cycles, between 1 to 2 kWh and a P/E between 15 to 20 or more.
! (Deutsche Bank 2009), p. 43 explains that ‘Advanced Lithium Ion Batteries’ can be grouped “into 4
broad categories, based on the formulation contained in the cathode […]” For Lithium Iron Phosphate
22!National!Research!Council! (2010).! Transitions! to!Alternative! Transportation!Technologies! E! PlugEin!Hybrid! Electric!Vehicles,! Committee! on! Assessment! of! Resource! Needs! for! Fuel! Cell! and! Hydrogen! Technologies:& 70! pages.! “A 50 percent range in SOC does, however, come at a price: The battery must have a nameplate capacity twice as high as the amount of energy actually needed and delivered to meet performance targets. In other words, the PHEV-40 will need a nameplate battery rating of 16 kWh to supply 8 kWh of the energy actually used for its [64 km] 40 miles of charge-depleting driving (or 20 kWh if it is oversized to account for 20 percent degradation).”!23!Wikipedia:! Chevrolet! Volt! (Dt.)! Chevrolet! Volt,!Wikipedia.:! “Die Lithium-Ionen-Batterie des Chevrolet Volt hat eine Kapazität von 16 kWh und ein Gewicht von 198 kg. Sie besteht aus 220 Zellen. Die Ladeelektronik ist programmiert, den Ladezustand der Batterie zwischen 30 und 85 % zu halten, um deren Lebensdauer zu verlängern, so dass effektiv nur 8,8 kWh genutzt werden. Trotz fast identischer Kapazität wiegt die Batterie des Chevrolet Volt fast 70 % weniger als die des bis 1999 gebauten General Motors EV1.”!24!“The!impact!of!prescribed!limitations!of!the!battery!(e.g.,!operational!SOC!range,!charge!and!discharge!power!as!a!function!of! SOC,! etc.)! on! the!AER! can!be!evaluated.! […]Battery! life! and!performance!degradation! can!be!predicted!from!the!results!of!the!AER!test.!Figure!10!shows!the!operation!of!the!battery!in!AER!range.!The!battery’s!initial!state!of!charge!was!89%,!and!the!battery!was!subjected!to!consecutive!urban!(UDDS)!cycles! in!allEelectric!mode!until! the!SOC!reached!29%.”!25!“PSAT!was!used!to!size!the!vehicle!so!that!the!battery!was!discharged!from!a!0.9!state!of!charge!(SOC)!to!a!0.3!SOC!when!the!vehicle!was!driven!20!mi!on!several!iterations!of!the!UDDS.”!
167
(LPF) (Deutsche Bank 2009), p. 45 reports that “Most iron phosphate batteries can be operated between
15% and 95% state of charge.”
! Compare (U.S. Department of Energy 2007), pp. 17-19 for a discussion about development goals for
batteries in dependence of different charging cycles for BEV, PHEV or RE batteries, i. a.: “The
requirements for a PHEV battery combine those of an electric vehicle (EV) which only depletes the
battery during operation (i.e., ‘charge depleting only’) and a typical HEV in production today that
maintains the battery state of charge within bounds (i.e., ‘charge sustaining’) […]. A PHEV battery will
experience both deep discharges like an EV […] and shallow cycling necessary to maintain the battery
for power-assist in charge sustaining HEV mode […]. In addition to the stringent duty cycle, the power-
to-energy (P/E) ratio (an influential design parameter) is specific to each vehicle application.”
12.3.2. Battery unit costs
This section provides a literature overview of reported battery unit costs in €/kWh.
! (National Research Council 2010), p. 54 reports “The committee reviewed a variety of sources to
establish the most probable and optimistic costs for the current generation of battery packs. The
review, discussed below, indicated a range of [425 €] $500 to [1.275 €] $1500/kWh nameplate.” and
“Based on this range, the committee selected [745 €/kWh] $875/kWh as the most probable value and
[530 €/kWh] $625 as an optimistic value for batteries that have already been ordered to be used in the
first generation of PHEV-40s, and [700 €/kWh] $825 and [530€/kWh] 625$ for PHEV-10s.”26
! The study of NRC about battery costs is contracted by the U.S. government who targets significantly
decreasing battery prices in time. NRC believes that the targets of the U.S. government are too high.
However, to meet his clients’ requirements, NRC takes an optimistic scenario with high declining
battery costs into consideration. Additionally, NRC provides results for an in NRC opinion more probable
scenario concerning the evolution of battery production costs.
! (Hensley, Knupfer et al. 2009), p. 88 and 89 report current battery pack costs of 700 to 1500 $/kwh
(equivalent 590 to 1270 €/kWh) and about 300 to 500 $/kWh (or 250 to 420 €/kWh) in 2020.
! (Kley, Wietschel et al. 2010), p. 11 assume battery investment costs before taxation of 502 €/kWh.
! (Deutsche Bank 2009), p. 45 reports: “Based on discussions with our auto, auto parts, and battery
Industry contacts, we believe that the median price for lithium ion ‘energy battery’ packs, which are
used in electric vehicle and some plug-in hybrid vehicles, is currently around the $650 per kwh range.
Power batteries, which are used in hybrid electric vehicles, are quoted in the $900-$1000 per kwh
range.” and on p. 48: “Power Battery’ packs, which are used in a hybrid (and some plug-in hybrid)
electric vehicles, are quoted in the $900-$1000 per kWh range today. The cost of this type of battery
may decline to $675 per kWh by 2015 and $450 per kWh by 2020 (A typical hybrid is equipped with the
equivalent of 1-2 kWh of batteries, and a plug-in hybrid may contain 8-16 kWh).”
! (Lowe, Tokuoka et al. 2010), p. 13 report for Li-ion batteries costs between 1.000 and 2.000 US$/kWh
for vehicles and costs between 300 and 800 US$/kWh for consumer electronics.
! (Wansart and Schnieder 2010), [p. 4] assume a battery price of 18.000 US$ and a battery unit price of
500 US$/kWh, so a battery capacity of 36 kWh.
Especially concerning the learning curves in battery development we found the following statements:
26!The!appendices!“E40s“!and!“E10s“!describe!the!allEelectric!range!(AER)!in!miles!of!these!vehicles,!so!40!miles!and!10!miles!AER.!
168
! (National Research Council 2010), p. 18 state: “Li-ion batteries based on similar technology are already
being produced in great numbers and are well along their learning curves. The steep early drop in cost
often experienced with new technologies is not likely. The incremental cost to manufacture these
vehicles is expected to decline by about one third by 2020 but only slowly thereafter […].”
! (Hensley, Knupfer et al. 2009), p. 88 claim that battery production costs decline by 6 to 8 percent
annually.
! (Brooker, Thornton et al. 2010), p. 8 assume 3% battery cost reductions per year.
! (AECOM Australia 2009), p. 10 reports in table 2-3 “Estimated cost of automotive lithium-ion batteries
at various production volumes (US $)”
! (Lowe, Tokuoka et al. 2010), p. 17 report: “According to Deutsche Bank, the cost of lithium-ion
batteries will decrease from $650/kWh in 2009 to $325/kWh by 2020 (Deutsche Bank 2009).”
12.3.3. Battery sizes
The following explanations about technology basics are cited from (Lowe, Tokuoka et al. 2010), p. 11:
“Battery performance requirements depend on the vehicle application. Two important factors determine battery
performance: energy, which can be thought of as driving range, and power, which can be thought of as acceleration.
The power-to-energy (P/E) ratio shows how much power per unit of energy is required for the application (DOE,
2007).
HEVs: Most HEVs use batteries to store energy captured during braking and use this energy to boost a vehicle’s
acceleration. The battery in an HEV is required to store only a small amount of energy, since it is recharged
frequently during driving. Batteries for HEVs have a “shallow cycle,”—which means they do not fully charge—and they
are designed for a 300,000-cycle lifetime. Because of these cycle characteristics, HEV batteries need more power than
energy, resulting in high P/E values ranging from 15 to 20. The battery capacity is relatively small, just 1-2 kilowatt-
hours (kWh) (DOE, 2007).
PHEVs: PHEVs are hybrid vehicles with large-capacity batteries that can be charged from the electric grid. With their
larger battery capacity, 5 to 15 kWh (DOE, 2007), PHEVs use only their electric motor and stored battery power to
travel for short distances, meaning that PHEVs do not consume any liquid fossil fuels for short trips if the batteries are
fully charged (Hori, 1998). After battery-stored energy is depleted, the battery works as an HEV battery for power
assisting. Thus, a PHEV battery needs both energy and power performance, resulting in a medium P/E range of 3-15.
In other words, PHEV batteries require both shallow cycle durability—similar to HEVs— and deep cycle durability.
EVs: EVs only use an electric motor powered by batteries to power the vehicle. Batteries for EVs need more energy
capacity because of longer driving ranges, so EVs have the lowest P/E factor. The battery gets fully charged and
discharged (deep cycles) and requires 1,000-cycle durability. The battery size of EVs is larger than that for PHEVs or
HEVs. For example, the Nissan Leaf has a 24-kWh capacity (Nissan USA, 2010). Lithium-ion battery packs for compact
EVs will use 1,800 to 2,000 cells (METI, 2009b).”
Evidences about typical battery sizes and other relevant attributes of electric vehicles (BEV, PHEV, RE and hybrids)
can be obtained from different information sources:
! manufactured cars actually sold in the market,
! statements of manufacturers,
! engineering experts.
169
• (U.S. Department of Energy 2007) report: “With the uncertainty in vehicle requirements, near- and long-term
goals are being developed. The near-term goals, drafted in collaboration with the USABC, target a 10 mile
all-electric range for a mid-size SUV, which implies a 5-10 kWh battery with approximately 40 kW peak
power, costing no more than $4,000. Mid-term goals will be established as PHEV requirements solidify. The
long-term goal is 40 mile all-electric range for a mid-size passenger car and the same $4,000 system cost.”
From this information source we take the information that battery sizes of PHEVs (which encompass PHEVs
and REs in our terms) could be 5 to 10 kWh in 2011.
12.3.4. Characteristics of selected BEV
Table&17&Characteristics&of&selected&BEV&
segment car kWh (Li-ion battery)
battery weight in kg, reported
battery weight in kg, est. (120 Wh/kg)
hp, reported
max. speed in km/h
acceleration in sec./100km
range in km, reported
curb weight in kg
hp, est. (1800 W/kg, 60% SOC range)
range in km, est.
Mini BMW Mini E
35 292 204 152 251 1465 232 149
Think City
24,5 204 100 160 1038 162 130
Smart Fortwo ED
16,5 138 40 100 26,7 135 955 109 92
Mitsubishi i-MiEV
16 133 64 130 160 1700 106 62
Peugeot iON
est. 16 133 64 130 130 1100 106 82
Citroen C-Zero
est. 16 133 64 130 130 1110 106 82
VW E-Up!
18 150 135 15,9 130 1085 119 93
Kompakt Ford Focus
23 192 134 136 121 1555 152 94
Nissan Leaf
24 200 200 107 145 160 1520 159 100
Mercedes-Benz A-Klasse E-Cell
36 300 68 150 14 200 238
Volvo Electric
24 280 200 112 130 150 1600 159 96
Geländewagen (SUV)
Luis 4U green
Lithium-Eisen-
208 37 120 200 1400 165 110
170
2010 Phosphat, 25
Sport Venturi America
Lithium-Ionen-Polymer, 54
450 450 299 180 300 1350 357 243
Tesla Roadster
56 450 467 288 212 3,9 340 1220 371 270
Protoscar LAMPO
33,6 280 280 268 200 1380 222 149
Porsche e-Ruf
51 540 425 225 7 250 1910 338 180
Mercedes SLS eDrive
48 420 400 571 >200 7 150 - 180
1620 318 191
Mini-Van Renault Kangoo Be Bop Z.E.
15 125 60 130 160 99
Utility Ford Transit Connect Electric
28 320 233 125 12 130 1791 185 104
Mercedes-Benz Vito E-Cell
36 288 300 82 80 130 2150 238 117
12.3.5. The weights of battery electric vehicles
Information about the weights of battery electric vehicles of different segments can be obtained from manufacturer
data, literature review.
Curb weights for Smart Fortwo BEV (955 kg) can be compared to (Smart Fortwo Diesel (850 kg) corresponding to a 12,4
% (Wikipedia: Smart Fortwo).
The weight of the Ford Focus ST BEV increases by using electric propulsion from 1437 kg to 1555 kg.27 This represents
a 8,2 %. increase.
(Blesl M., Bruchof D. et al. 2009), p. 31 claim about Kleinstwagen and Mittelklassewagen that contemporary fuel cell
hydrogen electric vehicles weight 10 percent more than gasoline cars and BEV weight 11 % more than diesel cars. They
claim that the excess weight will in the future be reduced to 6 percent due to weight reduction measurements.28
27!see!report!auto.de.msn.com!(2009)!"Jay!Lenos!KompaktEStromer:!Ford!Focus!ST!BEV."!28!Blesl!M.,!Bruchof!D.,!et!al.! (2009).!Entwicklungsstand!und!Perspektiven!der!Elektromobilität,!Universität!Stuttgart,!Institut! für! Energiewirtschaft! und! Rationelle! Energieanwendung:& 78.,! p.! 31! „Die% heutigen% schwersten% Kleinstwagen%bzw.%Mittelklassewagen% sind% FCHEV% [gemeint% sind% Fuel% Cell% Hydrogen% Electric% Vehicle]% (10%%%mehr%Gewicht% als% das%
OttoHReferenzfahrzeug)% bzw.% BEV% (11% %% mehr% Gewicht% als% das% DieselHReferenzfahrzeug).% Mit% den% diskutierten%
171
These various information suggest that the additional weight of BEV compared to conventional car is in the range 8-13
%
In complement with this literature review, we examined a series of existing cars. This suggested a larger excess
weight of BEV (up to 20%)29. Based on these conflicting evidences, it appears reasonable to use a figure in the higher
range of the interval identified by the literature, leading to a 13% extra weight assumption.
To summarize, BEV weight is estimated based on curb weights of (according to KBA) best seller gasoline vehicles + 13 %.
Turning now to battery weight, we assume they amount to 20 % of the curb weight of a BEV with the only exception of
sport cars, based on manufacturer data, we have to assume an higher percentage (29 %). The assumed battery weights
for BEV in different segments and the estimated values for BEV curb weights and battery weights are reported in other
appendix
Annahmen% zur% Reduktion% des% spezifischen% Gewichts% wird% das% schwerste% Fahrzeug% in% Zukunft% (FCEV)% nur% 6% %%
Mehrgewicht%gegenüber%seinem%Referenzfahrzeug%aufweisen.%
Aufgrund!des!allgemein!geringen!Mehrgewichts!der!alternativen!Antriebe!gegenüber!dem!Referenzfahrzeug!lässt!sich!festhalten,!dass!dieses!keine!Einschränkungen!hinsichtlich!der!Anwendbarkeit!dieser!Konzepte,!weder!heute,!noch!in!der!Zukunft!bedeutet.“!29! A! series! other! comparisons! suggest! an! higher! weight! increase! for! BEV,! where! the! weight! of! electric! cars! BEV!increases!by!about!20!percent!compared!to!a!gasoline!vehicle!of!the!same!segment:!The!median!curb!weight!of!BEV!Minis!in!our!data!amounts!1100!kg!and!1555kg!for!BEV!Compact!cars.!In!both!cases!these!values!are!about!20%!higher!than!the!average!curb!weights!surveyed!for!gasoline!Minis!(913!kg)!and!gasoline!Compact!cars!(1295!kg).We!rely!on!the!median!rather!than!the!mean!value!since!especially! in!the!phase!of! the!market! launch!of! these!new!propulsion!technology!vehicles,!cars!with!outlier!attributes!may!be!produced!which!affect!the!mean!attribute!value!significantly.!
172
12.3.6. Energy consumption of BEV
We make use of the following information sources:
! Anecdotal evidences on electric car energy requirements suggest 12,5 kWh/100km (National Research
Council 2010).
! (AECOM Australia 2009), p. 10 reports: “Recharging of EVs/PHEVs will require approx. 0.15-0.25
kWh/km depending upon vehicle size and powertrain efficiency. Recharging generally varies depending
upon the size and weight of the vehicle, as well as its powertrain efficiency and charging circuit
topology.”
! (Rousseau, Shidore et al. 2007), p. 4 give evidence about the energy consumptions in Wh/mile of
electric vehicles (midsize sedan, crossover, midsize SUV) in dependence of their vehicle masses in kg.
From the exhibit in (Rousseau, Shidore et al. 2007) it can be seen that the usable energy of a 1500 kg
midsize sedan and crossover is about 225 Wh/mile and 270 Wh/mile, respectively. Additionally,
(Rousseau, Shidore et al. 2007) show “[…] the impact of vehicle mass on the usable energy per unit of
distance. Note that the impact is similar from one configuration to another. For every 100 kg in vehicle
mass added, 10–11 Wh/mi are used.”
We conclude from (Rousseau, Shidore et al. 2007), p. 4 that an electric vehicle of 1500 kg curb weight
consumes 230 Wh/mile or 143 Wh/km and additional 10,5 Wh/mile or 6,5 Wh/km for each 100 kg excess
vehicle mass.
12.4. Battery capacities of Plug-in hybrid electric vehicles and Range extenders
Concerning the battery capacities of PHEV and RE we consider the following information sources:
! (Nemry and Leduc 2009), report:
For PHEVs, the energy storage requirement considered in the literature typically varies from 6 kWh to 30 kWh depending on the CD range (compared to 1-2 kWh for conventional hybrids and 30-50 kWh for BEVs). The energy storage capacity represents the 'available' or 'total' energy capacity depending on whether the SOC window is taken into account or not (e.g. a 10 kWh of total energy capacity operating with a 65% charge swing would have only 6.5 kWh of available energy). Generally, the battery usable energy increases linearly with the CD range [(Rousseau, Shidore et al. 2007)].
(Nemry and Leduc 2009), pp. 8, 9
! (National Research Council 2010) considers two types of vehicles named “PHEV-10” and “PHEV-40”
where the appendices 10 and 40 refer to the all-electric range (in miles) of the corresponding vehicle.
From the description of the technologies, the PHEV-10 can be considered as a Plug-in Hybrid Electric
Vehicles (PHEV) in our terms while the PHEV-40 can be considered as a Range Extender (RE). (National
Research Council 2010), p.9 anticipates that the PHEV-40 requires 8 kWh of energy for an all-electric
range (AER) of 64 kilometers (40 miles).
! (Faron, Pagerit et al. 2009) assume battery sizes of 4 kWh and 8 kWh for Power Split PHEV (PHEV in our
terms) and 12 kWh and 16 kWh for Series PHEV (RE in our terms).
173
Another source of information relates to specific marketed models:
! The (in our terms) range extender Chevrolet Volt manufactured by General Motors is equipped with a 16
kWh (10,4 kWh usable) lithium-ion battery pack.
! The (in our terms) PHEV Toyota Prius Plug-in is equipped with three Cobalt-Lithium-Ion-batteries (one
main and two sub batteries) with a total energy capacity of 5,2 kWh.
12.5. Energy consumption of Plug-in hybrid electric vehicles and Range extenders
We take into account the following information from car manufacturers and other studies about fuel consumptions of
PHEV and RE:
! (Toyota Deutschland GMBH) reports:
Jedes in dem Programm eingesetzte Prius Plug-in Hybridfahrzeug (PHV) legte im ersten Jahr durchschnittlich 19.000 Kilometer zurück. Damit liegen die Fahrzeuge deutlich über der durchschnittlichen Jahresfahrleistung französischer Autofahrer von 13.000 Kilometern. Dabei wurden Kraftstoffeinsparungen von 40 Prozent gegenüber Dieselfahrzeugen der gleichen Leistungsklasse erzielt. Die Reichweite des Prius Plug-in im rein elektrischen Betrieb ist mit 20 Kilometern völlig ausreichend, um den Großteil der täglichen Pendlerfahrten abzudecken. Die durchschnittliche Fahrtenlänge betrug 13,9 Kilometer.
(Toyota Deutschland GMBH)
Since the fuel consumption of a diesel compact car is pursuant to ADAC 4,8 l/100 km we take from this
report the information that the fuel consumption of the Prius Plug-in Hybridfahrzeug is about 2,9 l/100 km.
! Toyota reports in its newsletter "Weltpremiere: Die Studie Yaris HSD Concept - Toyotas Zukunft in
Europa" for the PHEV a fuel consumption of 2,6 liters in the test driving cycle “europäischer Testzyklus
NEFZ” and states that this amount is 30 percent less than the fuel consumption of the conventional
Prius.
! (Deutsche Bank 2009), p. 37 reports about the Toyota Prius PHEV a consumption of 5,1 l/100km.
! (Deutsche Bank 2009), p. 37 reports:
Overall, PHEVs are expected to have the ability to deliver a 40%-65% improvement in fuel economy (versus non-hybrid vehicles).
(Deutsche Bank 2009), p. 37
! Opel reports about the compact range extender Opel Ampera according to UN ECE R101 weighted fuel
consumptions of 1.6 l/100km and combined CO2 emissions of 40g CO2/km (preliminary information).
Additionally, Opel states:
“Opel estimates that Ampera drivers will save about 1700 liters of gasoline based on 60 km of daily driving (compared to a similar-sized vehicle with an average fuel consumption of around 7.8 l/100 km).”30
Additional, several statements can be found in the internet:
Bezüglich Verbrauch ist momentan nur eines wirklich klar: Während der ersten 60 km fährt sich der Ampera elektrisch und benötigt nur Strom, laut Opel sind dies im neuen europäischen Fahrzyklus NEFZ rund 13 kWh/100 km. Wie hoch der Verbrauch nach dem Einschalten des Verbrennungsmotors nach diesen 60 km ausfällt, darüber schweigt sich Opel bis auf eine wenig aussagekräftige Angabe von 1,6 L/100 km in einem 100 km langen NEFZ noch aus. Doch ist anzunehmen, dass sich die Verbrauchswerte im Verbrennungsmotorenbetrieb im Bereich eines normalen 1400er-Benziners bewegen werden. Einerseits läuft
30!see!http://www.opelEampera.com/index.php/mas/home.!
174
der Benziner zwar meist im ungedrosselten und somit verbrauchsgünstigen Bereich, anderseits muss doch ein mit rund 1800 kg Leergewicht recht schweres Fahrzeug bewegt werden. Die Verlustleistung mit Generator und E-Motor ist zwar naturgemäss sehr niedrig, aber trotzdem noch höher als bei einem Antrieb über ein Getriebe direkt auf die Räder.
(Martin)
! About the compact range extender Audi A1 etron it can be found in the internet:
Nach dem Normentwurf ergibt sich aus den beiden unterschiedlichen Betriebsarten ein Kraftstoffverbrauch von gerade einmal 1,9 l/100 km, er entspricht einer CO2-Emission von 45 g/km. Im elektrischen Betrieb liegt der unmittelbare CO2-Ausstoß bei null - der kompakte A1 e-tron fährt so ökologisch wie ökonomisch.
See, http://www.hybrid-autos.info/Elektro-Fahrzeuge/Audi/audi-a1-e-tron-2010.html
! (Moawad, Sharer et al. 2009), pp. 6-8 report fuel and energy consumptions for different propulsion
technologies:
Table 18 Fuel and energy consumptions of vehicles with alternative propulsion technologies, Source: (Moawad, Sharer et al. 2009): Impact of battery energy on fuel and electrical consumption
Technology Fuel consumption (l/ 100 km)
Electrical energy consumption (kWh/ 100 km)
Conventional 6,61 NN
HEV (Hybrids in MMEM terms)
4,69 NN
Split 4 kWh (PHEV in MMEM terms)
3,27 5
Split 8 kWh (PHEV in MMEM terms)
2,32 8,6
Series 12 kWh (RE in MMEM terms)
1,50 11,1
Series 16 kWh (RE in MMEM terms)
1,23 12,8
(Moawad, Sharer et al. 2009) made use of the following modeling assumptions and techniques:
Different powertrain configurations, including conventional, HEVs and several PHEVs were simulated on more than 110 real world daily drive cycles.
The power split configuration was selected for the HEV and PHEV 4 and 8 kWh cases, while the series option was used for the largest battery engines (12 and 16 kWh).
Significant fuel economy gains were demonstrated both with HEVs and PHEvs with fuel displacement increasing linearly with available electrical energy.
Benefits of adding 4 kWh of battery energy seems to decrease from 12 to 16 kWh due to the distribution of the daily driving distances.
(Moawad, Sharer et al. 2009), p. 15
! (AECOM Australia 2009), p. iii reports about the evolution of PHEV fuel consumption:
PHEVs [PHEV and RE in MMEM definitions] will use the electric drivetrain for 50% of kilometers in 2012 increasing to 80% in 2035.
(AECOM Australia 2009), p. iii
175
12.6. Fuel consumption of hydrogen vehicles
About the energy consumption of hydrogen vehicles we consider the following information:
! (TCS), p. 3 reports a hydrogen consumption of 3,5 kg/100 km for the BMW Hydrogen 7 which is equipped
with an internal combustion engine. About the other technology variant of hydrogen vehicles – equipped
with fuel cells - (TCS), p. 3 claims that at most sources report a consumption of about 1,5 kg/ 100 km.
So (TCS), p. 3 reports a consumption of 1,5 kg hydrogen/ 100 km for the Mercedes F-Cell relying on
ADAC, 4,2 kg hydrogen / 320 km range for the Opel Hydrogen4 relying on Automobil Revue,
Tagesanzeiger, somewhat more than 1 kg/ 100 km for the Nissan X-Trail FCV relying on Automobil Revue
and 4,5 kg hydrogen/ 320 km range for the Ford Hy-Series-Drive relying on Automobil Revue.
! (H2YDROGEIT) states:
Für den Vergleich verschiedener Treibstoffe sind auch deren Gewicht und Volumen relevant, da sie die Ausmaße der Treibstofftanks bestimmen. Um das Äquivalent des Brennwertes von 30 kg (40 l) Benzin mit zu führen, muß der Fahrer 60 kg (70 l) Methanol, 24 kg (ca. 110 l) Erdgas oder 10 kg (etwa 370 l) Wasserstoff tanken, wobei sich die zwei letztgenannten Werte auf komprimierte Gase bei 300 bar Druck beziehen. [PSI, 1998]
(H2YDROGEIT)
! (Deutscher Wasserstoff- und Brennstoffzellen-Verband 2010) reports:
Das optimierte Brennstoffzellensystem der neuesten Generation ist rund 40 % kleiner als das System in der ebenfalls im US-Praxisbetrieb gefahrenen A-Klasse F-CELL seit 2004, entwickelt aber 30 % mehr Leistung bei 30 % weniger Verbrauch. Die Kaltstartfähigkeit der B-Klasse F-CELL liegt bei -25 °C. Der 100 kW starke Elektromotor liefert ein Drehmoment von 290 Nm. Dabei erzielt er einen NEDC-Verbrauch (Neuer Europäischer Fahrzyklus) von umgerechnet nur 3,3 l Kraftstoff (Diesel-Äquivalent) je 100 km.
(Deutscher Wasserstoff- und Brennstoffzellen-Verband 2010), p. 11
! In another internet source it is reported:
Für den Vergleich verschiedener Treibstoffe sind auch deren Gewicht und Volumen relevant, da sie die Ausmasse der Treibstofftanks bestimmen. Um das Äquivalent des Brennwertes von 30 kg (40 l) Benzin mit zu führen, muss der Fahrer 60 kg (70 l) Methanol, 24 kg (ca. 110 l) Erdgas oder 10 kg (etwa 370 l) Wasserstoff tanken, wobei sich die zwei letztgenannten Werte auf komprimierte Gase bei 300 bar Druck beziehen. [PSI, 1998]
(H2YDROGEIT)
Since in the Market Model Electric Mobility the focus is on the fuel cell technology, we take from these information
sources that a (compact) car which consumes 6 l gasoline per 100 km would consume 1,5 kg hydrogen per 100 km.
Hydrogen consumptions for the other segments are calculated accordingly.
12.7. Fuel consumption of vehicles running on Biofuel (E85)
Several information sources about biofuel E85 consumption are considered:
! (Empa Dübendorf 2007) report:
Der volumetrische Kraftstoffverbrauch in [l/100km] stieg im Ethanolbetrieb im NEFZ um 28% was durch den geringeren Heizwert von Ethanol gegenüber Benzin hervorgerufen wird (siehe Treibstoffanalyse im Anhang). In allen gemessenen Zyklen zeigte sich eine Verbrauchssteigerung zwischen 24% und 30%. Der energetische
176
Kraftstoffverbrauch in [MJ/100km] sank jedoch im Ethanolbetrieb im NEFZ um 1.5% und verringerte sich in allen Zyklen zwischen 0.4% und 4.9%.
(Empa Dübendorf 2007), S. 5
! Another internet source reports:
The EPA has measured the gas mileage of 2006 flexible fuel models. For the 31 models they tested the average reduction is 26% fewer miles per gallon. For example a car that gets 30 mpg on regular would typically get 22.2 mpg with E85. This is exactly what is predicted from the fact that E85 has less energy per gallon than gasoline.
(zFacts.com)
! In another internet source it is explained:
Diesel has the highest energy content, which is at 40.9 MJ/L, among other fuels. This explains why diesel gives higher fuel economy. […]Gasoline has 32 MJ/L which results in a considerably high gas mileage. […]Compared to gasoline and diesel, the gas mileage in ethanol is the least. Ethanol only has 30.40 energy content, which yields about 34% less gas mileage than gasoline. Because E85 fuel only has around 80% of the energy of gasoline, full usage of the said fuel alternative remains to be in question. Certainly, the lack of energy content in E85, which accounts for the two percent to 30 percent loss in gas mileage, is not to be discounted especially by the public. Also, it has been observed with fuel combination, gas mileage drops as there is less gasoline present in the fuel mixture. Taking a look at gasohol, which is a mixture of 10% ethanol and 90% gasoline, energy content is at 28.06 MJ/L. If you will compare this E85, which is a mixture of 85% ethanol and 15% gasoline, energy content is lesser as the ethanol content is much higher than the gasoline. Now, you can only imagine how less the energy content is in pure ethanol. E100, which is the pure ethanol fuel, only has 19.59 MJ/L energy content.
(Clean Air Trust)
! The ADAC reports
Da Ethanol einen um rund 35% geringeren Energiegehalt als Ottokraftstoff hat, ist mit einem Mehrverbrauch von bis zu einem Drittel zu rechnen. Der Preis für E85 an den Tankstellen differiert regional stark, liegt aber durchschnittlich um ungefähr 20 Cent unter dem Niveau für herkömmliches Superbenzin.
(ADAC Bioethanol)
! In Wikipedia it is written:
Der Preis pro Liter ist jedoch in konventionellen Benzinfahrzeugen durch den bis zu 30-prozentigen Mehrverbrauch für den Verbraucher nicht unbedingt wirtschaftlicher als Eurosuper.[…]
Die FFV verbrauchen bei Betrieb mit E85 rund 35 vol% mehr Kraftstoff gegenüber dem Standardbenzinmodell bei Leistungssteigerungen bis etwa 20 % (Herstellerangaben). FFV können mit jeglicher Ethanol-Benzin-Mischung von 0 bis 85 % Ethanol betrieben werden
(Wikipedia Bioethanol)
From these information sources we conclude that the E85 consumption is 30 % higher compared to gasoline.
12.8. Consumption of vehicles running on LPG
The following information about the LPG consumption of cars were found in the internet:
! (Kraftstoff-Info.de) reports:
177
Der Preis für Autogas liegt in Deutschland zwischen 0,47 Euro und 0,65 Euro pro Liter. Auf Grund des geringeren volumetrischen Energiegehalts von LPG ergibt sich je nach Autogassystem im Gasbetrieb ein Mehrverbrauch von ca. 10% bis 20%.
(Kraftstoff-Info.de)
! In another internet source it is explained about CNG:
Erdgasfahrzeuge rechnen sich über die geringen Kraftstoffkosten. In der Anschaffung sind sie zwar teurer als die Dieselvariante, diese Mehrkosten amortisieren sich allerdings je nach Modell bereits ab einer jährlichen Laufleistung von 20.000 km über den Verbrauch von vergleichsweise günstigerem ERDGAS. Die Kosten liegen derzeit rund 50 Prozent niedriger als bei Superbenzin und etwa 30 Prozent unter dem aktuellen Dieselpreis. Auch gegenüber Flüssiggas beträgt die Kostenersparnis beim Tanken von ERDGAS bezogen auf den Energiegehalt rund 20 Prozent.
http://www.erdgas.info/erdgaskraftstoff/wirtschaftlichkeit/, call date Jan 5th, 2011
From these information sources we conclude that the LPG consumption is 20 % higher compared to gasoline.
HP#Hybrids#The (US Department of Energy 2007) reports about hybrids:
Drive motors/generators in today’s hybrids are packaged as fully integrated front-wheel drive (FWD) units, e.g., the original Prius (right) as well as in-line rear-wheel drive (RWD) units such as in the 2007 Lexus LS 600h or axle-mounted RWD units such as in the Lexus RX400h.
Power ranges from 50kWmax (at 1200-1540 rpm for the approximately 25kWcont Prius motor) up to 160kWmax for the Lexus LS 600h. But in all cases the electric traction motors provide about half the maximum power of their respective propulsion systems.
(US Department of Energy 2007), p. 22
HP bioethanol (E85) vehicles We consider several internet sources.
! In one it is claimed:
Auch erreicht man aufgrund der hohen Oktanzahl von Bioethanol einen besseren energetischen Wirkungsgrad des Motors, konkret steigt die Motorleistung um ca. 5 % (Beispiel Betrieb eines Ford Focus 1,6 l in FFV-Ausführung mit E85 im Vergleich zu bleifreiem Superbenzin: E85-Betrieb = 105 PS : Benzinbetrieb = 100 PS).
(http://www.e85-fahren.ch/faq.php)
! In another report it is written:
Die max. Motorleistung stieg im Ethanolbetrieb aufgrund der höheren Klopffestigkeit (siehe Treibstofffanalyse im Anhang) um 5% auf 98 kW und das max. Drehmoment hat sich um 6% auf 169 Nm erhöht.
(Empa Dübendorf 2007), S. 5
! The (Bundesverband der deutschen Bioethanolwirtschaft e. V.) claims:
Mit E85 im Tank (85 Prozent Bioethanol/15 Prozent Benzin) verfügt der neue 9-3 BioPower 1.8t-Motor über eine um 17 Prozent gesteigerte Höchstleistung (129 kW / 175 PS gegenüber 111 kW / 150 PS) und zehn Prozent mehr Drehmoment (265 statt 240 Nm) als im reinen Benzinbetrieb. Die 9-3 SportLimousine beschleunigt damit von null auf 100 km/h in 8,4 Sekunden und im fünften Gang von 80 auf 120 km/h in 13,9 Sekunden gegenüber 9,5 beziehungsweise 15,0 Sekunden im Benzinbetrieb.
(Bundesverband der deutschen Bioethanolwirtschaft e. V.)
! In Wikipedia it is written:
178
Die FFV verbrauchen bei Betrieb mit E85 rund 35 vol% mehr Kraftstoff gegenüber dem Standardbenzinmodell bei Leistungssteigerungen bis etwa 20 % (Herstellerangaben).
(Wikipedia Bioethanol)
We believe (Empa Dübendorf 2007) and assume an HP increase of 5 percent compared to gasoline vehicles.
Hydrogen technology
Technology definition: (TCS), p. 2 reports that it can be differentiated between hydrogen vehicles equipped with
combustion engines or fuel cells. (TCS), p. 2 claims that hydrogen vehicles equipped with combustion engines seem to
be less far away from serial production. But one disadvantage of this technology is that in the combustion process
oxides of nitrogen arise which have possibly to be cured. We refer to the fuel cell technology.
(TCS), p. 1 reports that it can be differentiated between tanks for fluent or compressed hydrogen. Fluent hydrogen is
saved at !253℃ while compressed hydrogen gas is saved with a pressure between 350 and 700 bar. (TCS), p. 1 reports
that 8 kg hydrogen can be saved fluent in a BMW Hydrogen 7 tank or 4,5 kg hydrogen can be saved compressed at 350
bar in a USA-Ford prototype “Hy-Series-Drive” tank. (TCS) claims that manufacturers intend to save hydrogen in the
future compressed at 700 bar. So 8 kg compressed at 700 bar instead of 4,5 kg compressed at 350 bar could be saved
in a tank of the same size as the Hy-Series-Drive tank. Additionally, the BMW Hydrogen can be propelled with
gasoline. (Inside Line) reports a tank capacity of 19,5 gallons or 74 liter. They report a range of 125 miles (201 km) on
hydrogen and 310 miles (499 km) on gasoline.
Concerning the attribute values of hydrogen vehicles we rely on several information sources:
Table 19 hydrogen vehicles, Source: (TCS) and (Inside Line)
vehicle hydrogen technology (ICE or fuel cell)
tank
capacity
assumed KBA-segment
range, reported by (Inside Line)
fuel consumption, reported by (TCS)
PS/kW
BMW Hydrogen 7
Internal combustion engine, bivalent hydrogen/gasoline ICE
8 kg hydrogen (fluent) and 19,5 gallons gasoline
Oberklasse 125 miles on hydrogen and 310 miles on gasoline
3.5 kg hydrogen/ 100 km
260 PS (restricted)
Mercedes F-Cell
1,5 kg hydrogen/ 100 km
Opel Hydrogen4
4,2 kg hydrogen 320 km calc.: 1,3125 kg/ 100 km
USA-Ford Prototyp “Hy-Series-Drive”
4,5 kg (compressed at 350 bar)
320 km calc.: 1,40625 kg/ 100 km
(The Connecticut Center for Advanced Technology Inc. 2011) reports the attr ibute values inter al ia of the fol lowing hydrogen vehicles:
179
Mercedes-Benz B-Class fuel cell vehicle has an electric motor with a peak performance of 100 kW/136 horsepower. This zero-emission fuel cell car consumes the equivalent of approximately one gallon of diesel fuel per 71 miles.[31, 32] The first 200 vehicles will be delivered to customers in Europe and the United States in 2010. Larger volume production is planned to begin in 2013 – 2015.
Kia’s second generation Fuel Cell Electric Vehicle (FCEV) is currently being demonstrated in their Borrego vehicle in the United States and Korea. Numerous innovations including a higher output 154 horsepower fuel cell and a 450-volt super capacitor give the Borrego FCEV higher performance, extended driving range and cold-weather starting capability to operate in sub-zero temperatures.[33]
BMW developed a hydrogen powered vehicle, the 7-Series that uses fuel from either a 19.5-gallon tank (gasoline) or a secondary tank with 8 kg (30 gallons) of liquid hydrogen. The BMW Hydrogen 7 Series is the first production-ready vehicle to be powered by liquid hydrogen, which has a range of 125 miles off its hydrogen tank and 300 miles off the gasoline tank.[34] BMW, in partnership with UTC Power, is also currently in development of a fuel-cell hybrid vehicle that will be used in the next generation Mini and a front wheel drive BMW vehicle is planned for release in 2014. The vehicle will generate power from a gasoline engine, a 5 kW hydrogen fuel cell drive train, and a 110 hp electric motor. This vehicle is designed to operate emission free when driving within cities.[35]
(The Connecticut Center for Advanced Technology Inc. 2011), p. 15, 16
Additionally, (The Connecticut Center for Advanced Technology Inc. 2011), p. 16 informs about attributes of more fuel
cell vehicles as tabulated:
(The Connecticut Center for Advanced Technology Inc. 2011), p. 16 reports hydrogen capacities in kg and ranges in
miles for hydrogen vehicles. For all hydrogen vehicles the hydrogen phase is gaseous with a hydrogen pressure of
10.000 psi. Only the hydrogen pressure of the Honda FCX Clarity amounts 5.000 psi. The following table is filled in
with assumptions and information from other sources which were found in the internet:
Table 20 Fuel cell vehicle attributes, Source: (The Connecticut Center for Advanced Technology Inc. 2011)
Vehicle Hydrogen capacity in kg
assumed KBA-segment
length in mm
kW curb weight
Chevy Equinox Fuel Cell
4.2 Geländewagen 4800 74 2010
Mercedes Benz B Class F-Cell
3.7 Mini-Van 4270 100 1355
Honda FCX Clarity 3.9 Obere Mittel- klasse
4830 100 1600
Hyundai Tucson ix35
5.6 Geländewagen/ Untere Mittelklasse
4410 120 1995
31!Daimler;!http://www.daimler.com/dccom/0E5E658451E1E1232162E1E0E0E0E0E0E11979E0E0E0E0E0E0E0E0.html!32!Car!and!Driver;!http://www.caranddriver.com/reviews/car/09q4/2011_mercedesEbenz_bEclass_fEcellfirst_!drive_review!33!Kia!Motors!Corporation;!http://www.kiamedia.com/secure/corporate112008b.html.!34!http://blogs.cars.com/kickingtires/2006/09/bmw_hydrogen.html.!35!UTC!Power;!http://www.utcpower.com/fs/com/bin/fs_com_Page/0,11491,0333,00.html.!
180
Kia Borrego 7.9 Geländewagen/ Obere Mittelkl.
4879 203 2023
Nissan X-Trail FCV 5.2 Geländewagen/ Untere Mittelklasse
4455 130 1790
Toyota FCHV 6 Gelände-wagen/ Mittel- klasse
4735 90 1860
According to the gasoline consumption the following tank sizes for hydrogen vehicles are calculated:
The gasoline consumption-tank size factor is assumed 0.6742.
Assuming 3 kg hydrogen capacity per 1000 kg curb weight and 64,5 kW per 1000 kW curb weight, 21,5 kW per kg hydrogen tank capacity are assumed: Table&21&
segment hydrogen tank capacity in kg
range in km
kW PS
Minis 3.4 337 72 99
Kleinwagen 3.6 347 78 106
Kompaktklasse 4.0 368 87 118
Mittelklasse 4.7 408 100 136
Obere Mittelklasse 5.1 425 109 148
Oberklasse 6.7 535 145 197
Geländewagen 6.3 515 136 185
Sportwagen 5.2 459 112 152
Mini-Vans 4.6 406 99 134
Großraum-Vans 4.6 399 99 134
Utilities 5.3 471 115 156
181
Hydrogen refueling stations and prices
(The Connecticut Center for Advanced Technology Inc. 2011), p. 28 assumes the following numbers about U.S.
refueling station projections including hydrogen prices:
Table 22 U.S. Refueling station and hydrogen price projections, Source: Table IV in (The Connecticut Center for Advanced Technology Inc. 2011), p. 28
Year 2010 2020 2035 2050
Number of refueling stations
60 669 19.100 110.300
Average capacity of new refueling stations (kg/day)
50 180 1.200 8.500
Kg of hydrogen dispensed/day
3.023 84.030 15,7 Mio. 362 Mio.
Price/gallon equivalent (hydrogen)
4,67 $/gallon or 1,05 €/l
3,91 $/gallon or 0,88 €/l
3,65 $/gallon or 0,82 €/l
3,58 $/gallon or 0,81 €/l
(Tetzlaff 2008), pp. 205 calculates costs (before taxes) for production of hydrogen from biomass of 2,5 €ct/kWh. For
the calculation of hydrogen transport costs (Tetzlaff 2008) considers data about the natural gas grid. (Tetzlaff 2008)
does not regard taxes, but concessions and profit margins (without monopoly profits).
Table 23 Hydrogen prices before taxes, Source: (Tetzlaff 2008)
€ct/kWh (heating value: Hu)
€ct/kWh ( Ho)
Private household 3,2 (2,7)
Industry 2,8 (2,4)
Alternative fuel refueling stations
! (Krail 2008) assumes the following numbers of stations for alternative fuel types:
182
Table 24 Assumptions about number of filling stations offering alternative fuel types in the EU27+2, Source: (Krail 2008), S. 113 Table 5-2
Fuel type 2008 2020 2030 2040
Conventional 115.000 113.379 112.802 112.802
CNG 3.678 38.365 49.646 49.646
LPG 14.120 19.904 19.965 19.965
Electric 1.052 26.400 27.965 30.660
Bioethanol 2.171 26.603 34.659 34.659
Hydrogen 73 297 2.166 6.593
! (The Connecticut Center for Advanced Technology Inc. 2011) models long term refueling station
projections by a Bass Diffusion Model:36
This Bass Diffusion Model is premised on a gross level assumption that 95 percent of the existing retail fueling stations will adopt hydrogen refueling capability by 2050. The existing market (m) is equal to the number of stations currently in existence in the United States, which are 121,466.
(The Connecticut Center for Advanced Technology Inc. 2011)
(Achtnicht 2010)
36 Bas s , F . M . (1969 ) . "A New Produc t Growth fo r Mode l Consumer Durab l e s . " Management Sc i ence 15 ( 5 ) : 215 -227 .
183
Further Information on fuel prices
Based on information from the Bundesverband der Energie- und Wasserwirtschaft e.V. (BDEW), (Wikipedia) reports
data about the composition of electricity tariffs (in €cent/kWh) in Germany from 1998 until 2011:37
Table&25&Historic&and&most&recent&values&of&electricity&prices&and&price&components&in&€cent/kWh&
Year Electricity generation and distribution
Concession levy
KWK Electricity tax
EEG contribution
VAT Electricity price pre-tax
Taxes (%)
1998 12.89 1.79 0 0 0.08 2.37 17.13 24.7%
1999 11.59 1.79 0 0.77 0.10 2.28 16.53 29.9%
2000 8.62 1.79 0.13 1.28 0.20 1.92 13.94 38.1%
2001 8.60 1.79 0.20 1.53 0.23 1.97 14.32 39.9%
2002 9.71 1.79 0.25 1.79 0.35 2.22 16.11 39.7%
2003 10.23 1.79 0.33 2.05 0.42 2.37 17.19 40.5%
2004 10.82 1.79 0.31 2.05 0.51 2.48 17.96 39.7%
2005 11.22 1.79 0.34 2.05 0.69 2.57 18.66 39.9%
2006 11.75 1.79 0.31 2.05 0.88 2.68 19.46 39.6%
2007 12.19 1.79 0.29 2.05 1.03 3.29 20.64 40.9%
2008 13.01 1.79 0.19 2.05 1.16 3.46 21.65 39.9%
2009 14.12 1.79 0.24 2.05 1.31 3.71 23.21 39.2%
2010 13.90 1.79 0.13 2.05 2.05 3.78 23.69 41.3%
*2011 13.90 1.79 0.13 2.05 3.53 4.06 25.46 45.4%
For a comparison: Fuel price scenarios in other studies
(Krail 2008), p. 100 assumes the following scenarios for future fuel price developments:
Scenarios for developments of fuel prices taken from POLES Model in BAU Scenario, Source: figure 5-1 in (Krail 2008)
Motor vehicle tax, petroleum tax, electricity tax, Value added tax (VAT)
The German (Bundesministerium der Finanzen 2010), pp. 44 - 45 reports the following types and rates of taxes:
! Motor vehicle tax (Kraftfahrzeugsteuer)
37 Source : h t tp ://de .w ik iped i a .o rg/w ik i/S t rompre i s , c a l l d a t e : J an . 5 th , 2011 .
184
Table& 26& Additional& charge& to& motor& vehicle& tax& for& each& 100& cm3& engine& displacement& for& compensation& of&petroleum&tax&for&diesel&engines&
- for passenger cars better than Euro-1
-other passenger cars
8,69 € 12,22 €
Initial registration July 1st, 2009 7,50 €
established for Jan 1s t , 1997 - June 30t h , 2009 ! Petroleum tax (Energiesteuer, until July 2006 designated “Mineralölsteuer”)
Table 27 Petroleum tax rates, Source: Bundesministerium der Finanzen 2010
Tax rates for fuel/ established in
January 1st, 2000
January 1st, 2001
January 1st, 2002
January 1st, 2003
Gasoline (lead free)
562,42 EUR / 1.000 l
593,10 EUR / 1.000 l
623,80 EUR / 1.000 l
654,50 EUR / 1.000 l
Diesel 378,36 EUR / 1.000 l
409,03 EUR / 1.000 l
439,70 EUR / 1.000 l
470,40 EUR / 1.000 l
Liquefied gas* 38,34 EUR / 1.000 kg
38,34 EUR / 1.000 kg
38,34 EUR / 1.000 kg
60,60 EUR / 1.000 kg
Natural gas* 3,476 EUR / MWh
3,476 EUR / MWh
3,476 EUR / MWh
5,50 EUR / MWh
Light fuel oil 61,35 EUR / 1.000 l
61,35 EUR / 1.000 l
61,35 EUR / 1.000 l
61,35 EUR / 1.000 l
*these tax rates do not coincide with the tax rates for l iquefied gas and natural gas used as motor fuels. Reduced tax rates are applied for l iquefied and natural gas used as motor fuels which are reported from the (Mineralölwirtschaftsverband e.V. 2011): 38
Table 28 Petroleum tax rates for motor fuels, Source: (Mineralölwirtschaftsverband e.V. 2011)
Tax rates for motor fuel/ established in
Gasoline, unleaded, ≤50/10 ppm (€/1000 l)
Diesel ≤50/10 ppm (€/1000 l)
Liquefied gas used as motor fuel 1)
(Autogas) (€/100kg)
Natural gas used as motor fuel 1)
(€/MWh)
01.11.2001 593,1 409,03 14,59 11,25
01.01.2002 623,8 439,7 15,34 11,8
01.01.2003 654,5 470,4 16,1 12,4
01.01.2004 654,5 470,4 18,03 13,9 1 ) Reduced tax rates since 31.Oct. 1995 are temporary for autogas unti l 31. Dec. 2009 and for natural gas unti l 31.Dec. 2020.
38 ADAC. "Autogas . " Re t r i eved 1 .9 .2011 , 2011 , f rom h t tp ://www.adac .de/ in fo te s t r a t/ t anken -k ra f t s to f f e -und-an t r i eb/au togas/de fau l t . a spx? t ab id=tab2 . r epor t s : „Der e rmäß ig t e S t eue r s a t z fü r d i e Nutzung von F lü s s i gga s a l s K ra f t s to f f zum Ant r i eb von Verb rennungsmoto ren in Fahrzeugen be t r äg t 9 ,7 Cen t/L i t e r (Benz in 65 ,4 Cen t ) . D ie s t eue r l i che Begüns t i gung wurde m i t dem neuen Energ i e s t eue rge se tz (Energ i eS tG) b i s zum 31 . Dezember 2018 f e s tge sch r i eben .“ (h t tp ://www.adac .de/ in fo te s t r a t/ t anken -k ra f t s to f f e -und-an t r i eb/au togas/de fau l t . a spx? t ab id=tab2 , c a l l d a t e 1 . Sep t . 2011 )
185
Based on our investigation no tax increase took place from 2004 onwards. In the reference scenario we assume time-invariant fuel taxes of 65,45 €ct/l gasoline, 47,04 €ct/l diesel and 18,03
€ct/kg autogas.
Additionally, numbers for the electric energy tax (Stromsteuer) are gained from the “Stromsteuergesetz”:
! Electric energy tax (Stromsteuer)
§ 3 StromStG:
„Steuertarif Die Steuer beträgt 20,50 Euro für eine Megawattstunde.“
(Stromsteuergesetz)
Table 29 Electricity tax (Stromsteuer) standard tax rate, Source: (Stromsteuergesetz)
established in tax rate
April 1st, 1999 10,20 EUR/ MWh
Jan 1st, 2000 12,78 EUR/ MWh
Jan 1st, 2001 15,34 EUR/ MWh
Jan 1st, 2002 17,90 EUR/ MWh
Jan 1st, 2003 20,50 EUR/ MWh
! Value added tax VAT (Umsatzsteuer) standard tax rate
Table 30 Umsatzsteuer (VAT), Source: (Stromsteuergesetz)
established period tax rate in percent
Jan 1st, 1968 -June 30th, 1968 10
July 1st, 1968 - Dec 31st, 1977 11
Jan 1st, 1978 - June 30th, 1979 12
July 1st, 1979 – June 30th, 1983 13
July 1st, 1983 – Dec 31st, 1992 14
Jan 1st, 1993 - March 31st, 1998 15
April 1st, 1998 – Dec 31st, 2006 16
since Jan 1st, 2007 19
We assume a time invariant VAT tax rate of 19 %.
The following data was found in the report of the Bundesministerium der Finanzen (Bonn, im Mai 2009), p. 8:
“Entwicklung der Energie- (vormals Mineralöl-) und Stromsteuersätze in der Bundesrepublik Deutschland”, published
in the internet:39
Table&31&Energy&and&electricity&tax&rates&
39 bundes f inanzmin i s t e r ium .de : En tw ick lung de r Energ i e - (vo rma l s M ine ra lö l - ) und S t roms teue r s ä t ze in de r Bundes repub l i k Deu t sch l and (h t tp :// www. bundes f inanzmin i s t e r ium . de/ nn_4192/ DE/ BMF__Sta r t s e i t e/ Se rv i ce/ Down loads/ Abt__IV/060 , t emp la t e Id=raw ,p rope r t y=pub l i c a t ionF i l e . pd f ) , c a l l d a t e December 15 th , 2010 . !
186
Date €/1000 kg LPG
January 1st, 2002 153,40
January 1st, 2003 161,00
January 1st, 2004 180,32
On the webside of the Bundesministerium der Justiz (http://bundesrecht.juris.de/energiestg/index.html) the legal
text of the so called “Energiesteuergesetz” was found:
Abweichend von Absatz 1 beträgt die Steuer […] für 1 MWh Erdgas und 1 MWh gasförmige Kohlenwasserstoffe […] bis zum 31. Dezember 2018 13,90 EUR, […] für 1000 kg Flüssiggase unvermischt mit anderen Energieerzeugnissen bis zum 31. Dezember 2018 180,32 EUR.
Energiesteuergesetz (EnergieStG), § 2 Steuertarif, Abs. 2
Composition of fuel prices The following numbers about fuel price components are based on (BMF 2005):
Table 32 Composition of fuel prices in Germany, Source: (BMF 2005)
Fuel price components €cent/liter (Super, August 2005)
€cent/liter (Diesel, August 2005)
Produkteinstandspreis (Superbenzin oder Diesel) 38,44 41,78
Marge (Transport-, Vertriebs-, Verw.kosten, Provisionen und Gewinn) 6,43 7,34
EBV (Beiträge an den Erdölbervorratungsverband Benzin 0,46 und Diesel 0,39 €cent/liter)
0,46 0,39
Mineralölsteuer (inkl. Ökosteuer) 65,45 47,04
Umsatzsteuer 17,72 15,45
Verbraucherpreis* 128,50 112,00
*monthly average price reported by the “Mineralölwirtschaftverband e.V.” (ARAL 2011) reports price components of recent Aral gasoline prices on its homepage:40 Table&33&ARAL&gasoline&price&components&
ARAL gasoline price components
3. January 2011 28. June 2011
Cent/l % Cent/l %
Wareneinstand / Produktpreis
50,6 34,45 53,3 35,56
Abgaben, Mehrwertsteuer, Energiesteuer, EBV
73,9 50,33 74,4 49,64
Ökosteuer 15,3 10,44 15,3 10,23
40 h t t p : / / w w w . a r a l . d e / a r a l / s e c t i o n g e n e r i c a r t i c l e . d o ? c a t e g o r y I d = 4 0 0 0 5 3 0 & c o n t e n t I d = 7 0 2 4 0 6 4 , ! ca l l d a t e s J an . 5 th , 2011 fo r the p r i c e componen t s on J anua ry 3 rd , 2011 and Ju l y 7 th , 2011 fo r the p r i c e componen t s on June 28 th , 2011 .
187
Kosten 7,0 4,78 6,8 4,57
Bioethanol price trends Development of service station prices for bioethanol E85 from July 2009 until July 2011, Source: (C.A.R.M.E.N. 2011).
Monthly bioethanol prices in Germany/Benelux and Rotterdam in 2009, Source: (Bundesverband der deutschen Bioethanolwirtschaft e.V.)
188
189
190
References
"Hohe Treibstoffpreise - Effekte auf das tägliche Mobilitätsverhalten."
Achtnicht (2010). Response to a ESMT request for further information on results of the research project ECO-CARS.
Achtnicht, M. (2008). German Car Buyers' Willingness to Pay to Reduce Co2 Emissions. SSRN eLibrary.
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Projektteam
! Michael! Holtermann! leitet! das! Forschungsprojekt! „Marktmodell! Elektromobilität“.! Seine!Erfahrungen! liegen! hauptsächlich! im! Bereich! regulierter! Märkte! und! Industrien,! von!Energiesystemen! und! Enetzwerken,! und! in! der! Interaktion! zwischen! privaten! und! öffentlichen!Institutionen.! Er! unterstützte! den! Präsidenten! der! ESMT,! Prof.! LarsEHendrik! Röller,! bei! dessen!Mitarbeit! im! Lenkungskreis! der! „Nationalen! Plattform! Elektromobilität“.! Davor! war! er! als!Programmdirektor! der! ESMT!Customized! Solutions!GmbH! (2004E2009),! als! Berater! bei!Accenture!im!Bereich!Telekommunikation,!Anwendungen!und!HighETech!vor!allem!im!Bereich!von!regulierten!Industrien! tätig! (1998E2004),! und! als!Mitarbeiter! von! Dr.! Klaus! von! Dohnanyi! bei! der! TreuhandENachfolgerin!BVS!mit!Marktzugangsfragen!für!privatisierte!ostdeutsche!Unternehmen!(1996E1998)!beschäftigt.!Sein!Studium!an!der!Freie!Universität!Berlin!beendete!er!mit!dem!Abschluss!MA.!
! Jörg!Radeke!ist!spezialisiert!auf!ökonomische!ModellierungsE!und!Vorhersageansätze!vor!allem!im!Kontext!der!Bewertung!von!wirtschaftlichen!Regulierungen!und!deren!komplexer!Auswirkungen.!Er!war! als!Wirtschaftsberater! beim! „centre! for! economics! and! business! research! (cebr)“! in! London!tätig! (2008E2010),! wo! er! volkswirtschaftliche! Analysen! und! Prognosemodelle! erstellt! hat,! zum!Beispiel! eine! Studie! für! die! britische! ‚Forestry! Commission‘! über! die! volkswirtschaftlichen! und!Umwelteinflüsse! von! verstärkter! Nutzung! von! erneuerbaren! Energien.! Die! Analyse! beinhaltete!neben!gesamtwirtschaftlichen!Aspekten,!auch!die!Frage!nach!möglichen!Treibhausgaseinsparungen!unter! unterschiedlichen! Politikszenarien.! Zudem!hat! er! Erfahrung! als! Berater! im!Bereich! privates!Eigenkapital! und! als! Dozent! für! wirtschaftswissenschaftliche! und! quantitative!Methoden.! Seinen!DiplomEAbschluss!erlangte!er!an!der!Universität!Rostock.!
! Dr.! Jens! Weinmann! ist! Projektmanager! des! BMUEProjekts! „Marktmodell! Elektromobilität“! und!Dozent! an! der! HWR! Berlin! und! der! HTW! Berlin! in! den! Fächern! Umweltökonomie! und! Statistik.!Seine! wissenschaftliche! Arbeit! hat! den! inhaltlichen! Fokus! Regulierung,! insbesondere! in! den!Bereichen! Energie! und! Transport.! Innerhalb! des! MMEM! ist! er! für! die! Umsetzung! der!Fragestellungen!zur!Wechselwirkung!zwischen!Elektroautos!und!der!Energieinfrastruktur!zuständig,!insbesondere!bezüglich!der!notwendigen!Erweiterungen!des!Verteilnetzes!im!Zuge!netzgesteuerter!Ladevorgänge! (VehicleEtoEGrid!und!GridEtoEVehicle).!Herr!Dr.!Weinmann! ist!DiplomEIngenieur! (TU!Berlin)!der!Fachrichtung!Energietechnik!und!promovierte!an!der!London!Business!School!im!Bereich!Entscheidungswissenschaften!!und!Quantitative!Methoden.!!
! Prof.!Dr.!Jérôme!Massiani!ist!ein!Transportökonom!mit!15Ejähriger!Berufserfahrung!im!Bereich!der!Modellierung.!Seine!Promotion! in!Wirtschaftswissenschaften!erlangte!er!an!der!UPEC!E!Université!ParisEEst! Créteil.! Prof.! Massiani! hat! an! verschiedenen! internationalen! Projekten! in! Frankreich,!Italien! und! Deutschland! gearbeitet,! darunter! Wirtschaftlichkeitsprüfungen! neuer! Infrastruktur,!Verkehrsprognosen,! Regulierungen,! sowie! KostenENutzenEAnalysen.! Aktuell! unterrichtet! er!Projektevaluation!und!Transportökonomie!an!der!Universität!Venedig.!!
! Giselmar! Hemmert! ist! DiplomEPhysiker! und! als! Analyst! für! das! Projekt! „Marktmodell!Elektromobilität“! tätig.! Seinen! Abschluss! erlangte! er! im! Jahre! 2009! an! der! Westfälischen!Wilhelmsuniversität! Münster.! Anschließend! war! er! bei! der! Swiss! Post! Solutions! im! Bereich!Consulting!tätig.!Seit! Januar!2011! ist!er!Teil!des!Projekts!und!hauptsächlich!verantwortlich!für!die!Modellierung!komplexer!Systeme.!Herr!Hemmert!wird!im!Anschluss!an!das!Projekt!seine!Promotion!beginnen.!!
196
! Andreas! Gohs! ist! DiplomEKaufmann! (GoetheEUniversität! Frankfurt! am! Main)! und! war! als!Researcher!für!das!Projekt!tätig!E!speziell!im!Bereich!der!Zeitreihenanalyse.!Von!2006E2009!war!er!ResearchEAssistent!am!Institut! für! Immobilienwirtschaft!der!Universität!Regensburg.! Im!Anschluss!an!das!Projekt!wird!Herr!Gohs!seine!Promotion!fortführen!
! Iris! Witsch! ist! seit! Dezember! 2010! studentische! Mitarbeiterin! im! Projekt.! Sie! studiert!Volkswirtschaftslehre!an!der!Freien!Universität!Berlin.!!
Das Projekt wurde in umweltökonomischen Fragen beratend unterstützt von Prof. Georg Meran. Prof. Meran hat an der Universität München, Universität Konstanz und an der Freien Universität Berl in studiert (Promotion 1986) und als wissenschaftl icher Mitarbeiter bzw. wissenschaftl icher Assistent gearbeitet . Die Habil i tat ion erfolgte 1993 an der Freien Universität . 1995 erhielt er einen Ruf an die TU Berl in. Seit 2004 ist Prof. Meran auch als Dean of Graduate Studies für das DIW Berl in tätig. Prof. Meran ist Wissenschaftl icher Sprecher des Forschungs-Centrums Netzindustrien und Infrastruktur (CNI) an der TU Berl in. Dank gi lt auch den Professoren der ESMT für ihre Unterstützung und Mitarbeit , insbesondere Sumitro Banerjee, Özlem Bedre-Defolie und Catal ina Stefanescu-Cuntze.
About ESMT
ESMT European School of Management and Technology was founded in October 2002 by 25 leading global companies
and institutions. The international business school offers Full-time MBA and Executive MBA programs, as well as
executive education in the form of open enrollment and customized programs. The business school works closely
together with ESMT Competition Analysis, which provides research-oriented consulting services in the areas of
competition and regulation. ESMT is a state-accredited private business school based in Berlin, Germany, with an
additional location in Schloss Gracht near Cologne.
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